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Which machine library do you use the most?

  • All
  • Catboost
  • Darts
  • Evidently
  • JAX
  • langgraph
  • LightGBM
  • null
  • PerpetualBooster
  • PyTorch/Lightning/Fast.ai
  • Sklearn
  • sktime
  • spaCy
  • synapse
  • TensorFlow
  • XGBoost

Which areas or modality of machine learning / science does your team work on?

Which use-cases does your team use machine learning for?

How long does it take your team to productionise a model (including data ingestion, etc)?

  • Less than 3 months
  • Less than 6 months
  • Less than a month
  • Less than a week
  • Less than a year
  • More than a year
  • null

Which cloud platform do you use the most?

  • Amazon Web Services
  • Azure
  • Cloudera
  • Databricks
  • Google Cloud Platform
  • Hetzner
  • IBM
  • IONOS
  • null
  • Self Hosting
  • Snowflake
  • SSPCloud

Select the top 3 biggest challenges that you face when productionising your machine learning models

For Model Registry and/or Experiment tracking, what tool do you use the most?

  • Azureml
  • Custom Built In-house tool
  • Data Version Control (DVC)
  • Evidently
  • Hopsworks Model Registry
  • MLFlow
  • null
  • Optuna
  • Snowflake
  • Spreadsheets
  • Weights & Biases

For Feature Store, what tool do you use the most?

  • azure ml's
  • Custom Built In-house tool
  • Custom Implementation
  • Databricks
  • databricks feature store
  • FEAST
  • Fennel
  • Hopsworks
  • null
  • Snowflake

For Vector Database, what tool do you use the most?

  • Azure AI
  • azure search
  • Chroma
  • ChromaDB
  • Custom Built In-house tool
  • Databricks
  • Elastic
  • Elasticsearch
  • Hopsworks
  • LanceDB
  • Milvus
  • null
  • OpenSearch
  • PG-Vector
  • PGVector
  • Pinecone
  • Qdrant
  • Quadrnt
  • Turbopuffer
  • Vespa
  • Weaviate

For ETL / Workflow Orchestrator, what tool do you use the most?

  • Agilab
  • Airflow
  • Amazon Glue
  • Argo Workflows
  • AWS Step Functions
  • Azure Data Factory
  • Azure ML Pipelines
  • Celery
  • Custom Built In-house tool
  • Dagster
  • Databricks
  • Flyte
  • GCP Vertex AI pipelines
  • GitHub actions
  • Hopsworks
  • Kubeflow
  • null
  • Oozie
  • Temporal
  • ZenML

For Model Training and Experimentation Platform, what tool do you use the most?

  • Agilab
  • Amazon SageMaker
  • Azure ML Studio
  • Cloudera
  • Cloudera CML
  • Custom
  • Custom Built In-house tool
  • Databricks
  • Domino Datalab
  • Evidently
  • Google Cloud Vertex AI
  • Hopsworks
  • Kedro
  • Lightning AI
  • Metaflow
  • MLFlow
  • null
  • Perpetual ML Suite

For Real Time Model Serving, what tool do you use the most?

  • Agilab
  • AWS
  • AWS Lambda
  • Azure managed online endpoints
  • Azure ML
  • BentoML
  • Custom Built In-house tool
  • Databricks
  • Django
  • Docker / Azure Functions
  • FastAPI/Flask Wrapper
  • KServe
  • LitServe
  • null
  • Nvidia triton
  • Sagemaker
  • Seldon Core
  • Tesseract Core
  • Triton
  • Triton inference server

For Model Monitoring, what tool do you use the most?

  • Agilab
  • Arize AI
  • AWS Clarify
  • Custom Built In-house tool
  • Databricks
  • Databricks lakehouse monitoring
  • Evidently AI
  • Grafana
  • Hopsworks
  • NannyML
  • NannyML plus custom library
  • Neptune AI
  • null
  • Perpetual
  • Tensorboard

For Central Data Platform / Data Lake, what tool do you use the most?

  • Agilab
  • AWS / Lakeformation
  • Azure DataLake
  • Cloudera CDP
  • Cloudera/HDFS/Hive
  • Custom Built In-house tool
  • Databricks
  • Databricks on Azure
  • Deltalake
  • Dremio
  • GCP / BigLake
  • null
  • Perpetual ML Suite
  • Snowflake
  • StarRocks

For Managed Foundation Model / LLM Api Services, what tool do you use the most?

  • All of the above
  • Amazon Bedrock
  • Anthropic
  • Azure AI
  • Cursor
  • Custom Built In-house tool
  • Databricks
  • DeepSeek V3
  • Fuel iX
  • Google Gemini
  • Nebius
  • null
  • Ollama
  • OpenAI

Which industry are you in?

  • Adtech, gaming
  • Aerospace & defense
  • AlcoBev
  • Automobile
  • Consultancy
  • Consultant
  • Consulting
  • Edu
  • Electronics
  • Energy
  • Engineering design
  • Financial services
  • Food
  • Government
  • government, official statistics
  • Healthcare
  • Hospitality
  • Industry
  • Insurance
  • insurtech
  • IT and Software Consultation
  • Manufacturing
  • Media & Entertainment
  • null
  • Retail
  • Risk Management
  • Technology
  • Telecommunications
  • Transportation & warehousing
  • Utilities

How large is your organisation?

  • 1,000-5,000 employees
  • 10-50 employees
  • 250-1,000 employees
  • 5,000-50,000 employees
  • 50,000+ employees
  • 50-250 employees
  • Less than 10 employees
  • null

How models does your organisation have in production? This can be a guesstimate and doesn't have to be an accurate number.

  • 0
  • 1
  • 10-20
  • 100-1000
  • 1000+
  • 2-5
  • 21-100
  • 5-9
  • null
  • Varies, we consult

In 12 months from today, how many models does your organisation plan to have in production by then? This can be a guesstimate and doesn't have to be an accurate number.

  • 0
  • 10-20
  • 100-1000
  • 1000+
  • 2-5
  • 21-100
  • 5-9
  • null

Select which of the following your organisation has set up:

What approximate percentage of your models run real-time INFERENCE instead of batch?

  • Between 10% and 30%
  • Between 50% and 90%
  • Less than 10%
  • More than 90%
  • null

When productionising a machine learning model, does your infrastructure enable any of the following deployment methods?

What is the "job family" of your role?

  • Business / Domain Practitioner
  • Data Engineer
  • Data Scientist
  • Machine Learning Engineer
  • MLOps Engineer
  • null
  • Product Manager
  • Software Engineer

How would you describe your role?

  • C-Suite
  • Director/VP
  • Individual Contributor (Junior to Senior)
  • Individual Contributor (Staff+)
  • Manager
  • null

What is your age?

  • 18-21
  • 22-24
  • 25-29
  • 30-34
  • 35-39
  • 40-44
  • 45-49
  • 50-54
  • 55-59
  • 60+
  • null

What is your country of residence?

  • Australia
  • Austria
  • Bangalore, INDIA
  • Belgium
  • Brazil
  • Canada
  • Chile
  • Danmark
  • Denmark
  • España
  • Finland
  • france
  • Germany
  • India
  • Indian
  • Ireland
  • Israel
  • Italy
  • Japan
  • Lebanon
  • Luxembourg
  • Mexico
  • N/A
  • Netherlands
  • Nigeria
  • nl
  • Norway
  • null
  • Pakistan
  • Poland
  • Portugal
  • Romania
  • Rwanda
  • South Africa
  • Spain
  • Sweden
  • Swiss
  • the Netherlands
  • Türkiye
  • UK
  • Ukraine
  • United Kingdom
  • united states
  • USA

How do you self identify?

  • Female
  • Male
  • null
  • Prefer not to disclose
  • Prefer to not disclose

Survey Data:20242025

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Which machine library do you use the most? Which areas or modality of machine learning / science does your team work on? Which use-cases does your team use machine learning for? How long does it take your team to productionise a model (including data ingestion, etc)? Which cloud platform do you use the most? Select the top 3 biggest challenges that you face when productionising your machine learning models For Model Registry and/or Experiment tracking, what tool do you use the most? Skip this question if you don't use any. For Feature Store, what tool do you use the most? Skip this question if you don't use any. For Vector Database, what tool do you use the most? Skip this question if you don't use any. For ETL / Workflow Orchestrator, what tool do you use the most? Skip this question if you don't use any. For Model Training and Experimentation Platform, what tool do you use the most? Skip this question if you don't use any. For Real Time Model Serving, what tool do you use the most? Skip this question if you don't use any. For Model Monitoring, what tool do you use the most? Skip this question if you don't use any. For Central Data Platform / Data Lake, what tool do you use the most? Skip this question if you don't use any. For Managed Foundation Model / LLM Api Services, what tool do you use the most? Skip this question if you don't use any. Which industry are you in? How large is your organisation? How models does your organisation have in production? This can be a guesstimate and doesn't have to be an accurate number. In 12 months from today, how many models does your organisation plan to have in production by then? This can be a guesstimate and doesn't have to be an accurate number. Select which of the following your organisation has set up: What approximate percentage of your models run real-time INFERENCE instead of batch? When productionising a machine learning model, does your infrastructure enable any of the following deployment methods? What is the "job family" of your role? How would you describe your role? What is your age? What is your country of residence? How do you self identify?
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PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Recommender Systems, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Search, Recommender systems, Fraud, Risk Less than 3 months Google Cloud Platform Inconsistency of training and experimentation environments, Building production-grade machine learning and data pipelines, Machine learning security Weights & Biases null null Airflow Google Cloud Vertex AI FastAPI/Flask Wrapper Custom Built In-house tool GCP / BigLake Google Gemini Technology 10-50 employees 2-5 10-20 Central Machine Learning Platform / Team, AI Risk & Governance Function Between 10% and 30% A/B Tests for Models, CI/CD for continuous deployment Machine Learning Engineer Individual Contributor (Staff+) 35-39 India Male
PyTorch/Lightning/Fast.ai Search, Time Series / Forecasting Demand Forecasting, Search Less than a month Azure Access to relevant data for training, Lack of specialised engineers, Showcasing business impact and business value MLFlow null null null Databricks Databricks null Deltalake Azure AI Healthcare 5,000-50,000 employees 2-5 10-20 Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) Less than 10% CI/CD for continuous deployment, Development-Staging-Production Environments Product Manager Director/VP 35-39 null Prefer to not disclose
synapse Tabular Marketing Intelligence, Risk, Pricing Less than 3 months Azure Building production-grade machine learning and data pipelines, Machine learning security, Governance and domain risks MLFlow Custom Built In-house tool null Custom Built In-house tool Databricks FastAPI/Flask Wrapper null Azure DataLake OpenAI Financial services 5,000-50,000 employees 100-1000 21-100 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Between 10% and 30% A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 30-34 null Male
Sklearn LLMs, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Pricing Less than a month Azure Inconsistency of training and experimentation environments, Machine learning monitoring and observability, Governance and domain risks MLFlow databricks feature store null Databricks Databricks Databricks Databricks Azure DataLake Azure AI Transportation & warehousing 5,000-50,000 employees 100-1000 100-1000 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Less than 10% CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Junior to Senior) 25-29 India Male
Sklearn Recommender Systems Recommender systems Less than a month Amazon Web Services Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Lack of specialised engineers MLFlow Custom Built In-house tool null Airflow Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool AWS / Lakeformation null Media & Entertainment 10-50 employees 10-20 null null More than 90% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Staff+) 35-39 Finland Male
Sklearn LLMs, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting Marketing Intelligence, Fraud Less than a month Google Cloud Platform null null null null ZenML null null null null null Technology 10-50 employees 2-5 2-5 null null null MLOps Engineer null 30-34 Germany Male
PyTorch/Lightning/Fast.ai Image / Computer Vision Anomaly detection Less than 6 months Google Cloud Platform Building production-grade machine learning and data pipelines, Governance and domain risks MLFlow Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Google Cloud Vertex AI FastAPI/Flask Wrapper Custom Built In-house tool GCP / BigLake Google Gemini Retail 5,000-50,000 employees 100-1000 5-9 Central Machine Learning Platform / Team null null MLOps Engineer Individual Contributor (Junior to Senior) 35-39 Swiss Male
PyTorch/Lightning/Fast.ai Recommender Systems Recommender systems Less than a month Amazon Web Services Lack of specialised engineers, Showcasing business impact and business value Weights & Biases null Elasticsearch Custom Built In-house tool Custom Built In-house tool Sagemaker null GCP / BigLake Google Gemini Media & Entertainment 1,000-5,000 employees 2-5 2-5 AI Inventory (Keeping track of all AI usecases and models) Between 50% and 90% A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 30-34 Japan Male
XGBoost LLMs, Search, Text / NLP (Non-LLM) Marketing Intelligence, Search, Risk Less than a month Amazon Web Services Inconsistency of training and experimentation environments, Access to relevant data for training, Building production-grade machine learning and data pipelines MLFlow null Weaviate Airflow Google Cloud Vertex AI FastAPI/Flask Wrapper null null null Risk Management 50-250 employees 5-9 10-20 AI Risk & Governance Function Less than 10% CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 22-24 India Male
Catboost Recommender Systems, Tabular, Time Series / Forecasting Recommender systems, Pricing Less than 3 months Amazon Web Services Showcasing business impact and business value, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability MLFlow FEAST Custom Built In-house tool Airflow Amazon SageMaker null null Snowflake Google Gemini Food 250-1,000 employees 5-9 5-9 null Less than 10% A/B Tests for Models, CI/CD for continuous deployment Machine Learning Engineer Individual Contributor (Junior to Senior) 35-39 Germany Male
JAX Physics-informed ML Simulations null Azure Gaps in tooling and support for model productionisation, Governance and domain risks, Lack of data Custom Built In-house tool null null GitHub actions Azure ML Studio Tesseract Core null null null Engineering design 10-50 employees null null Central Machine Learning Platform / Team null CI/CD for continuous deployment Machine Learning Engineer Individual Contributor (Staff+) 35-39 UK Male
Sklearn LLMs, Recommender Systems, Search, Tabular, Time Series / Forecasting, Simulations Demand Forecasting, Search, Recommender systems Less than a year Azure Governance and domain risks, incorporating the Machine Learning Model in the right place in the business process MLFlow null Azure AI Airflow Databricks FastAPI/Flask Wrapper Custom Built In-house tool Deltalake Azure AI Aerospace & defense 1,000-5,000 employees 10-20 21-100 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Between 10% and 30% CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Junior to Senior) 30-34 the Netherlands Male
XGBoost Recommender Systems, Text / NLP (Non-LLM) Marketing Intelligence, Fraud, Risk More than a year Google Cloud Platform Inconsistency of training and experimentation environments, Showcasing business impact and business value, Machine learning monitoring and observability MLFlow Custom Built In-house tool Custom Built In-house tool Airflow Amazon SageMaker Sagemaker AWS Clarify GCP / BigLake Google Gemini Telecommunications 5,000-50,000 employees 21-100 null Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Between 10% and 30% Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Junior to Senior) 30-34 Netherlands Male
TensorFlow Tabular, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Risk, Pricing Less than 6 months null Gaps in tooling and support for model productionisation, Lack of specialised data scientists, Building production-grade machine learning and data pipelines MLFlow null null Oozie Cloudera null null Cloudera/HDFS/Hive null Utilities 1,000-5,000 employees 2-5 2-5 Data Platform / Data Engineering Organisation, AI Risk & Governance Function Less than 10% Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 25-29 USA Male
PyTorch/Lightning/Fast.ai LLMs, Recommender Systems, Tabular, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Recommender systems, Risk, Pricing Less than 3 months Amazon Web Services Inconsistency of training and experimentation environments, Access to relevant data for training, Machine learning monitoring and observability Weights & Biases Hopsworks Pinecone Airflow Amazon SageMaker FastAPI/Flask Wrapper Arize AI Snowflake OpenAI Food Less than 10 employees 2-5 2-5 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) Less than 10% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment Machine Learning Engineer Individual Contributor (Junior to Senior) 22-24 india Male
Sklearn Causal Inference, LLMs, Recommender Systems, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Recommender systems, Fraud Less than 6 months Google Cloud Platform Access to relevant data for training, Machine learning monitoring and observability, Governance and domain risks Custom Built In-house tool Custom Built In-house tool Turbopuffer Custom Built In-house tool Google Cloud Vertex AI Custom Built In-house tool Arize AI GCP / BigLake Fuel iX Technology 50,000+ employees 100-1000 100-1000 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) null A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments null Individual Contributor (Junior to Senior) 30-34 Canada Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs AI Products Less than a week Google Cloud Platform null Custom Built In-house tool null null Flyte null Custom Built In-house tool Custom Built In-house tool null null Technology 50-250 employees null null Central Machine Learning Platform / Team null Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Staff+) 30-34 Germany Male
langgraph LLMs, Speech to text Marketing Intelligence, Customer support Less than 3 months Google Cloud Platform Showcasing business impact and business value, Machine learning monitoring and observability, Machine learning security null null null Airflow null FastAPI/Flask Wrapper null Custom Built In-house tool OpenAI insurtech 50-250 employees 1 2-5 Central Machine Learning Platform / Team, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) More than 90% CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Director/VP 30-34 Spain Male
XGBoost Causal Inference, LLMs, Search, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Fraud, Pricing Less than 6 months Amazon Web Services Access to relevant data for training, Building production-grade machine learning and data pipelines, Machine learning security Weights & Biases FEAST Custom Built In-house tool Amazon Glue Amazon SageMaker Sagemaker Neptune AI Custom Built In-house tool Amazon Bedrock Automobile 1,000-5,000 employees 2-5 21-100 A Developer Productivity Team which also covers machine learning, AI Inventory (Keeping track of all AI usecases and models) Between 50% and 90% Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 22-24 India Male
PyTorch/Lightning/Fast.ai Recommender Systems Risk Less than a week Azure Lack of specialised data scientists, Building production-grade machine learning and data pipelines, Machine learning security Spreadsheets Custom Built In-house tool Pinecone Celery Custom Built In-house tool Custom Built In-house tool Arize AI AWS / Lakeformation Google Gemini Aerospace & defense 10-50 employees 5-9 10-20 A Developer Productivity Team which also covers machine learning Between 10% and 30% Progressive Rollouts Business / Domain Practitioner Director/VP 35-39 India Male
Sklearn LLMs Fraud, Risk Less than a month Azure Access to relevant data for training, Machine learning monitoring and observability, Governance and domain risks MLFlow null Elasticsearch Azure ML Pipelines Azure ML Studio Azure managed online endpoints null null Azure AI Government 5,000-50,000 employees 21-100 5-9 AI Risk & Governance Function More than 90% Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Staff+) 30-34 UK Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Tabular, Text / NLP (Non-LLM) AI Products More than a year SSPCloud Inconsistency of training and experimentation environments, Lack of specialised engineers, Machine learning monitoring and observability MLFlow Custom Built In-house tool null Argo Workflows null FastAPI/Flask Wrapper null null null government, official statistics 1,000-5,000 employees 5-9 10-20 null Less than 10% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Staff+) 40-44 france Male
XGBoost LLMs, Recommender Systems, Tabular, Time Series / Forecasting Demand Forecasting, Recommender systems, Fraud Less than a month Amazon Web Services Lack of specialised engineers, Showcasing business impact and business value, Machine learning monitoring and observability Hopsworks Model Registry Hopsworks Hopsworks Hopsworks Hopsworks KServe NannyML Deltalake Google Gemini Media & Entertainment 5,000-50,000 employees 21-100 100-1000 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Between 10% and 30% Canary Deployments, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Staff+) 40-44 Ireland Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Search, Risk, Anomaly detection Less than 3 months Amazon Web Services lack of general engineers MLFlow null Milvus Airflow MLFlow KServe Evidently AI Custom Built In-house tool Anthropic Aerospace & defense 5,000-50,000 employees 100-1000 1000+ A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, AI Risk & Governance Function Less than 10% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Junior to Senior) 40-44 united states Male
XGBoost LLMs, Tabular, Text / NLP (Non-LLM) Demand Forecasting, Marketing Intelligence, Pricing Less than 3 months Amazon Web Services Showcasing business impact and business value, Building production-grade machine learning and data pipelines null null Azure AI Dagster Google Cloud Vertex AI null null Deltalake Azure AI Consulting 5,000-50,000 employees 10-20 10-20 A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Less than 10% Canary Deployments, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Director/VP 40-44 Japan Male
Darts Time Series / Forecasting Demand Forecasting Less than a month IONOS Access to relevant data for training Weights & Biases Custom Built In-house tool null Airflow Custom Built In-house tool Custom Built In-house tool null null null Retail 10-50 employees 21-100 100-1000 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Less than 10% null Software Engineer C-Suite 30-34 Germany Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Recommender Systems, Tabular, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Recommender systems, Fraud, Risk Less than a week Amazon Web Services Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability Weights & Biases null Weaviate Amazon Glue Amazon SageMaker FastAPI/Flask Wrapper Evidently AI AWS / Lakeformation OpenAI Insurance 5,000-50,000 employees 5-9 2-5 Data Platform / Data Engineering Organisation Between 10% and 30% Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment MLOps Engineer Individual Contributor (Staff+) 45-49 Bangalore, INDIA Male
PyTorch/Lightning/Fast.ai LLMs, Recommender Systems, Search, Text / NLP (Non-LLM) Marketing Intelligence, Recommender systems Less than 3 months Azure Showcasing business impact and business value, Machine learning monitoring and observability, Governance and domain risks MLFlow null Elasticsearch Databricks Databricks FastAPI/Flask Wrapper Custom Built In-house tool Deltalake OpenAI Consultancy 10-50 employees 5-9 10-20 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Less than 10% A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments Data Scientist Individual Contributor (Staff+) 45-49 Netherlands Male
LightGBM LLMs, Tabular, Time Series / Forecasting Fraud Less than a month Amazon Web Services Machine learning monitoring and observability, Machine learning security, Governance and domain risks Custom Built In-house tool Custom Built In-house tool null Airflow Custom Built In-house tool Triton null Custom Built In-house tool Amazon Bedrock Financial services 250-1,000 employees 21-100 100-1000 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation More than 90% Canary Deployments, Progressive Rollouts, CI/CD for continuous deployment Software Engineer Director/VP 35-39 Portugal Female
Sklearn LLMs, Recommender Systems, Tabular, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Recommender systems Less than 3 months Databricks Inconsistency of training and experimentation environments, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability MLFlow null null Databricks Databricks Databricks null Deltalake OpenAI Media & Entertainment 5,000-50,000 employees 5-9 10-20 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Between 10% and 30% A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments Data Scientist Individual Contributor (Staff+) 35-39 Canada Male
XGBoost Tabular AI Products Less than a month Amazon Web Services Machine learning monitoring and observability MLFlow Custom Built In-house tool null null Amazon SageMaker Seldon Core null null null Transportation & warehousing 250-1,000 employees 2-5 5-9 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation More than 90% CI/CD for continuous deployment MLOps Engineer Individual Contributor (Junior to Senior) 30-34 France Female
spaCy Search, Text / NLP (Non-LLM) Search, Information Extraction null Azure null null null LanceDB null null FastAPI/Flask Wrapper null null Ollama Telecommunications 50-250 employees null null null null CI/CD for continuous deployment Software Engineer Individual Contributor (Junior to Senior) 30-34 Germany Male
Sklearn Image / Computer Vision, LLMs, Recommender Systems, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Search, Recommender systems Less than a month Amazon Web Services Inconsistency of training and experimentation environments, Access to relevant data for training, Building production-grade machine learning and data pipelines Data Version Control (DVC) null Pinecone Airflow Amazon SageMaker Sagemaker null Snowflake OpenAI null null null null null null null Software Engineer Individual Contributor (Junior to Senior) 22-24 null Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Recommender Systems, Tabular, Time Series / Forecasting Demand Forecasting, Search, Recommender systems, Risk, Pricing Less than a month Google Cloud Platform Inconsistency of training and experimentation environments, Access to relevant data for training, Machine learning security MLFlow Fennel Milvus Airflow Amazon SageMaker BentoML Neptune AI Snowflake Google Gemini Technology Less than 10 employees 2-5 5-9 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Between 10% and 30% A/B Tests for Models, CI/CD for continuous deployment Machine Learning Engineer Individual Contributor (Junior to Senior) 35-39 Nigeria Male
Sklearn LLMs, Recommender Systems, Search, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Search, Recommender systems Less than 3 months Google Cloud Platform Gaps in tooling and support for model productionisation, Access to relevant data for training, Showcasing business impact and business value Data Version Control (DVC) Custom Built In-house tool Weaviate Airflow Google Cloud Vertex AI FastAPI/Flask Wrapper Evidently AI GCP / BigLake Google Gemini Consulting 1,000-5,000 employees 10-20 21-100 A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation Less than 10% Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment Data Scientist Individual Contributor (Staff+) 35-39 Mexico Male
PyTorch/Lightning/Fast.ai Time Series / Forecasting Embedded Systems Less than a week Amazon Web Services Inconsistency of training and experimentation environments, Access to relevant data for training, It/ot integration MLFlow null null Airflow Databricks null Custom Built In-house tool Deltalake null Manufacturing 1,000-5,000 employees 21-100 100-1000 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Less than 10% A/B Tests for Models, CI/CD for continuous deployment Machine Learning Engineer Director/VP 40-44 Germany Male
TensorFlow LLMs, Text / NLP (Non-LLM) Search, Fraud, Risk Less than 3 months Amazon Web Services Gaps in tooling and support for model productionisation, Lack of specialised engineers, Building production-grade machine learning and data pipelines MLFlow null ElasticSearch Airflow Kedro AWS Grafana StarRocks Amazon Bedrock Financial services 10-50 employees 2-5 5-9 Data Platform / Data Engineering Organisation Between 50% and 90% CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 35-39 Netherlands Male
PyTorch/Lightning/Fast.ai Causal Inference, Image / Computer Vision, LLMs Demand Forecasting, Search Less than 3 months Azure Lack of specialised engineers, Building production-grade machine learning and data pipelines, Governance and domain risks MLFlow null Azure AI Azure Data Factory Azure ML Studio FastAPI/Flask Wrapper null Azure DataLake OpenAI Healthcare 1,000-5,000 employees 100-1000 10-20 Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) Less than 10% CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Manager 35-39 India Male
Sklearn Image / Computer Vision, LLMs Risk Less than 3 months Amazon Web Services Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines null null null null Custom Built In-house tool KServe null null Azure AI Technology 10-50 employees 10-20 21-100 null Between 50% and 90% Canary Deployments, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Manager null Israel Male
TensorFlow Causal Inference, Image / Computer Vision, LLMs Intelligence Video Analytics Less than a year Google Cloud Platform Gaps in tooling and support for model productionisation, Access to relevant data for training, Machine learning security null null null null Custom Built In-house tool Triton inference server null Deltalake OpenAI Aerospace & defense 10-50 employees 2-5 2-5 A Developer Productivity Team which also covers machine learning Between 10% and 30% CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 35-39 Rwanda Male
Sklearn LLMs, Recommender Systems, Search, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Search, Recommender systems Less than 3 months Azure Access to relevant data for training MLFlow null Pinecone null Azure ML Studio FastAPI/Flask Wrapper Evidently AI Snowflake Azure AI Energy 5,000-50,000 employees 21-100 5-9 Central Machine Learning Platform / Team Between 50% and 90% CI/CD for continuous deployment Machine Learning Engineer Individual Contributor (Staff+) 40-44 Danmark Male
Sklearn Causal Inference, LLMs Risk Less than 3 months Azure Lack of specialised data scientists, Showcasing business impact and business value MLFlow Custom Built In-house tool Azure AI Airflow Databricks Databricks Evidently AI Azure DataLake OpenAI Financial services 5,000-50,000 employees 2-5 2-5 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function More than 90% Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Junior to Senior) 25-29 germany Male
Sklearn LLMs, Time Series / Forecasting Pricing, Information Extraction Less than 6 months Azure Showcasing business impact and business value, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability MLFlow null Milvus Airflow Azure ML Studio FastAPI/Flask Wrapper Neptune AI Deltalake OpenAI Healthcare 50,000+ employees 10-20 10-20 Central Machine Learning Platform / Team, AI Inventory (Keeping track of all AI usecases and models) Less than 10% A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 35-39 germany Male
Sklearn Causal Inference Demand Forecasting Less than 3 months Google Cloud Platform Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines MLFlow FEAST Pinecone Airflow Azure ML Studio BentoML Evidently AI Deltalake Azure AI Hospitality 50-250 employees 10-20 5-9 A Developer Productivity Team which also covers machine learning Less than 10% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Director/VP 45-49 India Male
null Causal Inference Search null Amazon Web Services Inconsistency of training and experimentation environments null null null null null null null null null null 250-1,000 employees null null null null null null null null null null
PyTorch/Lightning/Fast.ai Causal Inference, Image / Computer Vision, LLMs, Recommender Systems, Search, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Search, Recommender systems, Fraud, Risk, Pricing, Less than a week Google Cloud Platform Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Governance and domain risks null Custom Built In-house tool Pinecone Custom Built In-house tool null KServe null Snowflake Google Gemini Transportation & warehousing null 10-20 10-20 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) Between 50% and 90% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments Business / Domain Practitioner C-Suite 40-44 South Africa Male
Sklearn Causal Inference, Image / Computer Vision, LLMs, Recommender Systems, Search, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Search, Recommender systems, Fraud, Risk, Pricing Less than a week Google Cloud Platform Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines MLFlow null Pinecone Custom Built In-house tool Custom Built In-house tool KServe Evidently AI Azure DataLake OpenAI Utilities 250-1,000 employees 10-20 21-100 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) Between 50% and 90% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Director/VP 40-44 South Africa Male
Lightgbm Image / Computer Vision, Tabular, Time Series / Forecasting AI Products Less than 6 months Google Cloud Platform Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Access to relevant data for training MLFlow null null Airflow Google Cloud Vertex AI FastAPI/Flask Wrapper Evidently AI GCP / BigLake null Utilities 5,000-50,000 employees 10-20 10-20 Central Machine Learning Platform / Team Less than 10% Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 25-29 null Male
PyTorch/Lightning/Fast.ai Tabular Fraud Less than 3 months Amazon Web Services Access to relevant data for training MLFlow Custom Implementation null Temporal Custom Custom Built In-house tool Custom Built In-house tool AWS / Lakeformation OpenAI Technology 10-50 employees 2-5 2-5 A Developer Productivity Team which also covers machine learning More than 90% A/B Tests for Models Machine Learning Engineer Director/VP 40-44 Australia Male
Sklearn LLMs, Tabular, Time Series / Forecasting Demand Forecasting Less than a week Google Cloud Platform Lack of specialised data scientists, Machine learning security, Governance and domain risks MLFlow null Pinecone Dagster null AWS Lambda null null OpenAI Consultancy 50-250 employees 2-5 0 AI Risk & Governance Function Less than 10% CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 30-34 null null
Sklearn Causal Inference, LLMs, Recommender Systems, Search, Tabular, Text / NLP (Non-LLM) Chatbot Less than 3 months Azure Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Access to relevant data for training Spreadsheets null Pinecone Amazon Glue Amazon SageMaker FastAPI/Flask Wrapper null null OpenAI Financial services 10-50 employees 2-5 2-5 A Developer Productivity Team which also covers machine learning Between 10% and 30% null Software Engineer Individual Contributor (Staff+) 22-24 india Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Tabular, Text / NLP (Non-LLM) Search, Fraud, Risk, Patterb Matching Less than 3 months Azure Governance and domain risks MLFlow FEAST Azure AI Airflow Azure ML Studio Custom Built In-house tool Custom Built In-house tool Snowflake Azure AI Financial services 50,000+ employees 100-1000 100-1000 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team Less than 10% A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment Machine Learning Engineer Manager 30-34 null Male
null LLMs Marketing Intelligence, Recommender systems, Risk Less than a month Azure Lack of specialised data scientists, Building production-grade machine learning and data pipelines Data Version Control (DVC) Custom Built In-house tool null null Azure ML Studio Databricks null Azure DataLake Anthropic Technology 10-50 employees 2-5 5-9 Data Platform / Data Engineering Organisation More than 90% Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments Business / Domain Practitioner C-Suite 50-54 Usa Male
Sklearn Recommender Systems, Time Series / Forecasting Demand Forecasting, Recommender systems Less than 3 months Azure Showcasing business impact and business value, Machine learning monitoring and observability, Governance and domain risks MLFlow null null Databricks Databricks Databricks Custom Built In-house tool Deltalake Azure AI Retail 50,000+ employees 1000+ 21-100 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) Less than 10% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Manager 30-34 Netherlands Female
XGBoost Causal Inference, Recommender Systems, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Recommender systems, Fraud, Risk, Pricing Less than 3 months Cloudera Inconsistency of training and experimentation environments, Showcasing business impact and business value, Machine learning monitoring and observability MLFlow null null Airflow Cloudera CML null Evidently AI Cloudera CDP null Financial services 50,000+ employees 21-100 10-20 Central Machine Learning Platform / Team Less than 10% A/B Tests for Models Data Scientist Director/VP 35-39 India Male
Sklearn Tabular, Text / NLP (Non-LLM) Demand Forecasting, Recommender systems Less than 6 months Azure Inconsistency of training and experimentation environments, Access to relevant data for training, Machine learning monitoring and observability MLFlow Databricks Azure AI Databricks Databricks Databricks Databricks lakehouse monitoring Azure DataLake Azure AI Transportation & warehousing 5,000-50,000 employees 100-1000 21-100 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Less than 10% CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Junior to Senior) 25-29 India Male
TensorFlow Image / Computer Vision, LLMs, Text / NLP (Non-LLM) Demand Forecasting, Search, Risk Less than 3 months Google Cloud Platform Access to relevant data for training, Lack of specialised engineers, Machine learning security Custom Built In-house tool Custom Built In-house tool Pinecone Airflow Google Cloud Vertex AI BentoML Neptune AI GCP / BigLake Google Gemini Technology 50,000+ employees null null Central Machine Learning Platform / Team Between 10% and 30% Canary Deployments, CI/CD for continuous deployment MLOps Engineer Individual Contributor (Junior to Senior) 25-29 INDIA Male
Sklearn Causal Inference, Recommender Systems, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Recommender systems Less than 3 months Azure Inconsistency of training and experimentation environments, Showcasing business impact and business value, Governance and domain risks MLFlow FEAST null Azure Data Factory null null null Azure DataLake null Consulting Less than 10 employees 5-9 10-20 A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation Less than 10% CI/CD for continuous deployment, Development-Staging-Production Environments Data Scientist Individual Contributor (Junior to Senior) 40-44 South Africa Male
LightGBM Causal Inference, Time Series / Forecasting Demand Forecasting, Pricing Less than a month Azure Inconsistency of training and experimentation environments, Showcasing business impact and business value, Machine learning monitoring and observability MLFlow Databricks Azure AI Databricks Databricks Databricks Custom Built In-house tool Azure DataLake Azure AI Retail 50,000+ employees 21-100 21-100 Data Platform / Data Engineering Organisation Between 10% and 30% Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 25-29 Netherlands Male
null LLMs Marketing Intelligence Less than 3 months Amazon Web Services Showcasing business impact and business value, Machine learning monitoring and observability, Governance and domain risks Spreadsheets null null Airflow Amazon SageMaker Sagemaker Custom Built In-house tool Snowflake Google Gemini Technology 1,000-5,000 employees 21-100 21-100 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Less than 10% CI/CD for continuous deployment, Development-Staging-Production Environments Software Engineer Manager 35-39 N/A Male
PyTorch/Lightning/Fast.ai LLMs, Search, Text / NLP (Non-LLM) Search, AI Products Less than a month Amazon Web Services Lack of specialised engineers, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability MLFlow null PGVector Databricks Databricks Databricks null Deltalake Amazon Bedrock Technology 50-250 employees 5-9 10-20 Central Machine Learning Platform / Team, AI Inventory (Keeping track of all AI usecases and models) Between 50% and 90% CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Manager 25-29 Germany Male
Sklearn LLMs, Recommender Systems, Tabular Demand Forecasting, Recommender systems, Pricing Less than 3 months Google Cloud Platform Machine learning monitoring and observability, Governance and domain risks MLFlow null null Airflow null null Evidently AI Snowflake Nebius Technology 50-250 employees 5-9 10-20 Central Machine Learning Platform / Team Between 10% and 30% null Machine Learning Engineer null 35-39 UK Female
Evidently Causal Inference, LLMs, Time Series / Forecasting Demand Forecasting, Marketing Intelligence Less than 3 months Google Cloud Platform Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Lack of specialised data scientists Evidently Custom Built In-house tool Pinecone Custom Built In-house tool Evidently Custom Built In-house tool Evidently AI Snowflake Custom Built In-house tool Technology 10-50 employees 10-20 21-100 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team Between 10% and 30% A/B Tests for Models Machine Learning Engineer Individual Contributor (Staff+) 35-39 United Kingdom Prefer not to disclose
Sklearn Causal Inference, LLMs, Time Series / Forecasting Demand Forecasting, Recommender systems Less than 3 months Azure Lack of specialised engineers, Machine learning monitoring and observability, Machine learning security MLFlow null null null Databricks Databricks null Azure DataLake Azure AI AlcoBev 50,000+ employees 10-20 21-100 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Less than 10% A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments Data Scientist Individual Contributor (Staff+) 30-34 South Africa Male
PyTorch/Lightning/Fast.ai LLMs, Search, Text / NLP (Non-LLM) Marketing Intelligence, Search Less than 3 months Amazon Web Services Inconsistency of training and experimentation environments null null null null null null null null null Healthcare 10-50 employees 2-5 2-5 null null null null null null null null
sktime Time Series / Forecasting Demand Forecasting Less than 6 months Azure Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability MLFlow null null Azure Data Factory Databricks null Arize AI Azure DataLake OpenAI Food 50,000+ employees 100-1000 100-1000 null Less than 10% CI/CD for continuous deployment Data Scientist Manager 40-44 Spain Male
All All Embedded Systems More than a year Self Hosting Feasibility MLFlow null Custom Built In-house tool Agilab Agilab Agilab Agilab Agilab OpenAI Aerospace & defense 50,000+ employees 100-1000 100-1000 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) More than 90% CI/CD for continuous deployment Machine Learning Engineer Individual Contributor (Staff+) 55-59 null Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Time Series / Forecasting Embedded Systems Less than a year Azure Governance and domain risks MLFlow null PGVector Airflow Azure ML Studio FastAPI/Flask Wrapper null Azure DataLake Anthropic Industry 10-50 employees 10-20 21-100 null Between 50% and 90% CI/CD for continuous deployment, Development-Staging-Production Environments Data Scientist Individual Contributor (Junior to Senior) 25-29 Austria Male
XGBoost LLMs, Search, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Search, Fraud, Risk Less than 3 months Azure Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Access to relevant data for training MLFlow Custom Built In-house tool Databricks Databricks Databricks Databricks Databricks Azure DataLake Custom Built In-house tool Financial services 50,000+ employees 21-100 100-1000 Central Machine Learning Platform / Team Less than 10% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments Data Scientist Individual Contributor (Junior to Senior) 25-29 null Female
LightGBM Tabular Marketing Intelligence, Fraud, Risk Less than 6 months Databricks Inconsistency of training and experimentation environments, Building production-grade machine learning and data pipelines, Infrastructure/policies constraints MLFlow null null Databricks Databricks Databricks NannyML plus custom library Databricks on Azure Azure AI Utilities 1,000-5,000 employees 2-5 10-20 Data Platform / Data Engineering Organisation Less than 10% CI/CD for continuous deployment, Development-Staging-Production Environments Data Scientist Individual Contributor (Junior to Senior) 30-34 Italy Male
XGBoost Tabular, Time Series / Forecasting Demand Forecasting, Marketing Intelligence Less than 3 months Amazon Web Services Lack of specialised engineers, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability MLFlow null null Airflow Amazon SageMaker Sagemaker null AWS / Lakeformation null Media & Entertainment 250-1,000 employees 100-1000 100-1000 Data Platform / Data Engineering Organisation Less than 10% CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Junior to Senior) 60+ Poland Male
PyTorch/Lightning/Fast.ai LLMs Safety Testing Less than a week Google Cloud Platform Machine learning security Custom Built In-house tool null Custom Built In-house tool Custom Built In-house tool Google Cloud Vertex AI FastAPI/Flask Wrapper Custom Built In-house tool null Google Gemini Telecommunications Less than 10 employees 2-5 5-9 Data Platform / Data Engineering Organisation, AI Risk & Governance Function Between 50% and 90% CI/CD for continuous deployment Data Engineer C-Suite 40-44 United States Male
Catboost Image / Computer Vision, LLMs, Recommender Systems, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Recommender systems Less than 3 months Google Cloud Platform Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines, Machine learning security Data Version Control (DVC) Hopsworks Weaviate Airflow Azure ML Studio Databricks Evidently AI Azure DataLake Azure AI Healthcare 250-1,000 employees 2-5 2-5 Central Machine Learning Platform / Team, AI Risk & Governance Function Between 10% and 30% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Staff+) 22-24 Indian Female
Sklearn LLMs, Recommender Systems, Tabular Recommender systems, Predictive ML Less than a month Amazon Web Services Inconsistency of training and experimentation environments, Access to relevant data for training, Showcasing business impact and business value MLFlow null Pinecone null null FastAPI/Flask Wrapper null null Amazon Bedrock IT and Software Consultation 50-250 employees 2-5 2-5 Central Machine Learning Platform / Team More than 90% CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 25-29 Pakistan Male
XGBoost Recommender Systems, Tabular, Time Series / Forecasting Demand Forecasting, Recommender systems, Fraud Less than a week Self Hosting Access to relevant data for training, Lack of specialised engineers, Showcasing business impact and business value Hopsworks Model Registry Hopsworks Hopsworks Airflow Hopsworks KServe Hopsworks Dremio DeepSeek V3 Telecommunications 5,000-50,000 employees 100-1000 1000+ Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Less than 10% Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 30-34 Sweden Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Text / NLP (Non-LLM) Information Extraction Less than 6 months IBM Lack of specialised engineers, Machine learning monitoring and observability, Legacy SDLC constraints Data Version Control (DVC) Custom Built In-house tool null Custom Built In-house tool Domino Datalab Django Custom Built In-house tool null Custom Built In-house tool Financial services 5,000-50,000 employees 100-1000 100-1000 AI Risk & Governance Function More than 90% CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Staff+) 30-34 Belgium Male
XGBoost Recommender Systems, Tabular, Time Series / Forecasting Fraud, Security Less than a week Amazon Web Services Access to relevant data for training, Building production-grade machine learning and data pipelines Weights & Biases Custom Built In-house tool Custom Built In-house tool Databricks Databricks FastAPI/Flask Wrapper Neptune AI Deltalake OpenAI Technology 1,000-5,000 employees 10-20 10-20 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation More than 90% Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment Data Scientist Manager 35-39 Israel Male
PerpetualBooster Causal Inference, Recommender Systems, Tabular, Time Series / Forecasting Demand Forecasting, Recommender systems, Fraud Less than 3 months Snowflake Building production-grade machine learning and data pipelines, Machine learning monitoring and observability, Machine learning security Snowflake Snowflake Quadrnt Airflow Perpetual ML Suite FastAPI/Flask Wrapper Perpetual Perpetual ML Suite Google Gemini Financial services 10-50 employees 2-5 5-9 Central Machine Learning Platform / Team Between 10% and 30% Canary Deployments, Development-Staging-Production Environments Machine Learning Engineer C-Suite 35-39 Türkiye Male
XGBoost LLMs, Time Series / Forecasting Demand Forecasting Less than 3 months Azure Access to relevant data for training MLFlow null Chroma Databricks Databricks FastAPI/Flask Wrapper null Azure DataLake Databricks Retail 1,000-5,000 employees 5-9 10-20 Data Platform / Data Engineering Organisation Less than 10% null Data Scientist Individual Contributor (Junior to Senior) 35-39 Italy Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Recommender Systems, Search, Text / NLP (Non-LLM) Search, Recommender systems, Risk, Classification Less than 3 months Google Cloud Platform Access to relevant data for training, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability MLFlow null Elastic Custom Built In-house tool Google Cloud Vertex AI FastAPI/Flask Wrapper Custom Built In-house tool null Google Gemini Technology 10-50 employees 10-20 10-20 null More than 90% CI/CD for continuous deployment, Development-Staging-Production Environments Data Scientist C-Suite null null null
PyTorch/Lightning/Fast.ai Causal Inference, LLMs, Time Series / Forecasting Demand Forecasting, Recommender systems, Pricing Less than a month Google Cloud Platform Showcasing business impact and business value, Machine learning monitoring and observability, Governance and domain risks MLFlow null Custom Built In-house tool Airflow Google Cloud Vertex AI FastAPI/Flask Wrapper Evidently AI GCP / BigLake OpenAI Technology 5,000-50,000 employees 21-100 10-20 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Less than 10% A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 25-29 Chile Male
Sklearn Causal Inference, Tabular, Time Series / Forecasting Risk Less than 3 months Amazon Web Services Access to relevant data for training, Lack of specialised engineers, Building production-grade machine learning and data pipelines MLFlow Custom Built In-house tool null null null null null null null Transportation & warehousing 10-50 employees 1 2-5 null null null Data Scientist Individual Contributor (Junior to Senior) 35-39 USA Female
Sklearn Causal Inference, Image / Computer Vision, Recommender Systems AI Products Less than 3 months Google Cloud Platform Access to relevant data for training, Building production-grade machine learning and data pipelines null null null null null null null null null Technology Less than 10 employees 0 2-5 Central Machine Learning Platform / Team Less than 10% null Machine Learning Engineer Individual Contributor (Junior to Senior) 18-21 India Male
Sklearn LLMs, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Search, Recommender systems, Pricing Less than 3 months Azure Access to relevant data for training, Lack of specialised engineers, Building production-grade machine learning and data pipelines MLFlow Custom Built In-house tool Milvus Custom Built In-house tool Azure ML Studio FastAPI/Flask Wrapper Evidently AI Snowflake Custom Built In-house tool Energy 1,000-5,000 employees 21-100 10-20 null Between 10% and 30% CI/CD for continuous deployment Machine Learning Engineer Individual Contributor (Staff+) 40-44 Denmark Male
PyTorch/Lightning/Fast.ai Recommender Systems, Tabular Recommender systems Less than 3 months Google Cloud Platform Inconsistency of training and experimentation environments, Access to relevant data for training Custom Built In-house tool Custom Built In-house tool null GCP Vertex AI pipelines Google Cloud Vertex AI Nvidia triton Custom Built In-house tool Custom Built In-house tool null Adtech, gaming 1,000-5,000 employees 100-1000 100-1000 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Between 50% and 90% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments Data Scientist Individual Contributor (Junior to Senior) 35-39 Finland Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Text / NLP (Non-LLM) Translation Less than 6 months Amazon Web Services Building production-grade machine learning and data pipelines MLFlow Custom Built In-house tool null Custom Built In-house tool Custom Built In-house tool FastAPI/Flask Wrapper null Custom Built In-house tool null Telecommunications 5,000-50,000 employees 2-5 5-9 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Less than 10% Development-Staging-Production Environments MLOps Engineer Individual Contributor (Junior to Senior) 35-39 Brazil Male
PyTorch/Lightning/Fast.ai LLMs, Search, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Search, Recommender systems, Pricing Less than 3 months Azure Access to relevant data for training, Lack of specialised engineers, Governance and domain risks MLFlow Custom Built In-house tool Custom Built In-house tool Airflow Azure ML Studio FastAPI/Flask Wrapper Custom Built In-house tool AWS / Lakeformation Azure AI Food 5,000-50,000 employees 100-1000 100-1000 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Between 10% and 30% CI/CD for continuous deployment Data Scientist C-Suite 40-44 Spain Male
PyTorch/Lightning/Fast.ai Causal Inference, Image / Computer Vision, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Recommender systems Less than 6 months Self Hosting Gaps in tooling and support for model productionisation, Lack of specialised engineers, Showcasing business impact and business value Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Edu 250-1,000 employees 10-20 21-100 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Less than 10% Progressive Rollouts, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Staff+) 50-54 Australia Male
Sklearn Tabular Fraud, Risk, Pricing Less than a week Amazon Web Services Inconsistency of training and experimentation environments, Access to relevant data for training, Building production-grade machine learning and data pipelines MLFlow Custom Built In-house tool null Databricks Metaflow FastAPI/Flask Wrapper Custom Built In-house tool Deltalake Amazon Bedrock Financial services 1,000-5,000 employees 21-100 21-100 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Between 50% and 90% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Staff+) 30-34 USA Male
TensorFlow LLMs, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Search Less than a year Amazon Web Services null null null null null null null null null null null null null null null null null null null null null null
XGBoost Recommender Systems, Search, Time Series / Forecasting Demand Forecasting, Search, Recommender systems, time estimation Less than a week Google Cloud Platform Machine learning monitoring and observability MLFlow FEAST Pinecone Airflow Google Cloud Vertex AI KServe Evidently AI Custom Built In-house tool OpenAI Retail 250-1,000 employees 10-20 21-100 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Between 50% and 90% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments Data Scientist Director/VP 50-54 Germany Female
PyTorch/Lightning/Fast.ai Causal Inference, LLMs, Recommender Systems, Search Search, Recommender systems, Fraud, Risk Less than a year Azure Gaps in tooling and support for model productionisation, Access to relevant data for training, Lack of specialised data scientists MLFlow null Azure AI Airflow Databricks Databricks null Deltalake OpenAI Financial services 5,000-50,000 employees 21-100 100-1000 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) null null Business / Domain Practitioner Manager 40-44 Netherlands Male
Catboost Recommender Systems, Search, Tabular, Time Series / Forecasting Demand Forecasting, Search, Recommender systems Less than a month Google Cloud Platform Inconsistency of training and experimentation environments, Machine learning monitoring and observability MLFlow FEAST null Airflow Google Cloud Vertex AI KServe Evidently AI GCP / BigLake Google Gemini Retail 1,000-5,000 employees 5-9 10-20 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Between 10% and 30% Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments Data Scientist Manager 25-29 Germany Male
Sklearn Time Series / Forecasting Demand Forecasting Less than 3 months Azure Access to relevant data for training, Showcasing business impact and business value, Machine learning monitoring and observability Azureml null null null Azure ML Studio null null Azure DataLake null Utilities 50,000+ employees 5-9 10-20 Central Machine Learning Platform / Team Less than 10% Development-Staging-Production Environments MLOps Engineer Individual Contributor (Staff+) 35-39 null Male
PyTorch/Lightning/Fast.ai LLMs, Recommender Systems Search, Recommender systems Less than 3 months Amazon Web Services Access to relevant data for training, Machine learning monitoring and observability, Governance and domain risks MLFlow FEAST Pinecone Dagster Amazon SageMaker BentoML Evidently AI Snowflake OpenAI Financial services 50-250 employees 10-20 21-100 A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation, AI Risk & Governance Function null Canary Deployments, A/B Tests for Models Data Scientist Director/VP 45-49 UK Male
Sklearn Tabular, Time Series / Forecasting Fraud, Risk Less than a month Amazon Web Services Inconsistency of training and experimentation environments, Machine learning monitoring and observability MLFlow FEAST Custom Built In-house tool Argo Workflows Databricks FastAPI/Flask Wrapper Evidently AI Deltalake OpenAI Financial services 10-50 employees 2-5 2-5 Data Platform / Data Engineering Organisation Between 10% and 30% CI/CD for continuous deployment Data Engineer Individual Contributor (Staff+) 30-34 Brazil Female
TensorFlow LLMs AI Products Less than a month null Access to relevant data for training, Lack of specialised data scientists Weights & Biases null null null null null null null null Insurance Less than 10 employees 2-5 5-9 A Developer Productivity Team which also covers machine learning null null Data Engineer Manager 55-59 Lebanon Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, Recommender Systems, Search Search, Recommender systems Less than a week Self Hosting Lack of specialised data scientists Custom Built In-house tool Custom Built In-house tool Vespa null null null Custom Built In-house tool Snowflake OpenAI Technology 1,000-5,000 employees 10-20 21-100 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Between 50% and 90% Canary Deployments, A/B Tests for Models Data Scientist Individual Contributor (Staff+) 30-34 null Male
Sklearn LLMs, Tabular, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Risk Less than a month Azure Inconsistency of training and experimentation environments, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability MLFlow null null null Azure ML Studio FastAPI/Flask Wrapper null null Azure AI Technology 1,000-5,000 employees 21-100 21-100 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Between 10% and 30% CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 30-34 Spain Male
Sklearn Recommender Systems, Tabular, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Recommender systems Less than 6 months Amazon Web Services Lack of specialised engineers, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability MLFlow FEAST null Airflow Amazon SageMaker null null Snowflake Google Gemini Food 250-1,000 employees 10-20 10-20 Data Platform / Data Engineering Organisation Less than 10% Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 35-39 Germany Male
PyTorch/Lightning/Fast.ai Image / Computer Vision Embbeded systems Less than 3 months Google Cloud Platform Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Access to relevant data for training Weights & Biases null null null null FastAPI/Flask Wrapper null null OpenAI Transportation & warehousing 5,000-50,000 employees 2-5 2-5 A Developer Productivity Team which also covers machine learning Less than 10% A/B Tests for Models Business / Domain Practitioner Individual Contributor (Staff+) 35-39 Romania Male
XGBoost Tabular Risk, Pricing Less than 6 months Azure Inconsistency of training and experimentation environments, Showcasing business impact and business value, Machine learning monitoring and observability null null null null Databricks null Evidently AI null OpenAI Insurance 1,000-5,000 employees 21-100 21-100 A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation Between 10% and 30% null Data Scientist Individual Contributor (Junior to Senior) null null Female
PyTorch/Lightning/Fast.ai Tabular, Time Series / Forecasting Demand Forecasting, Pricing Less than a month Google Cloud Platform Inconsistency of training and experimentation environments, Access to relevant data for training, Lack of specialised data scientists null null null null Google Cloud Vertex AI null Custom Built In-house tool GCP / BigLake null Retail null null null null null null Machine Learning Engineer Individual Contributor (Junior to Senior) null null null
Sklearn LLMs, Time Series / Forecasting Demand Forecasting null Amazon Web Services Lack of specialised data scientists, Showcasing business impact and business value null null Elasticsearch Airflow Amazon SageMaker null null AWS / Lakeformation Amazon Bedrock Telecommunications 250-1,000 employees 2-5 2-5 A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation Less than 10% null Product Manager Manager 35-39 España Male
PyTorch/Lightning/Fast.ai Causal Inference, Image / Computer Vision, Recommender Systems, Search, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Search, Recommender systems, Fraud, Pricing Less than a month Amazon Web Services Machine learning monitoring and observability, Machine learning security, Governance and domain risks MLFlow Custom Built In-house tool Custom Built In-house tool Databricks Databricks FastAPI/Flask Wrapper Custom Built In-house tool Databricks All of the above Technology Less than 10 employees Varies, we consult 10-20 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, AI Inventory (Keeping track of all AI usecases and models) Between 50% and 90% A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment Data Scientist C-Suite 40-44 Netherlands Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Time Series / Forecasting Demand Forecasting, Risk, Pricing Less than a month Amazon Web Services Gaps in tooling and support for model productionisation, Showcasing business impact and business value, Machine learning monitoring and observability Optuna Custom Built In-house tool OpenSearch Custom Built In-house tool Custom Built In-house tool null null Custom Built In-house tool Amazon Bedrock Hospitality 1,000-5,000 employees 5-9 10-20 Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) Between 10% and 30% A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment Data Scientist Director/VP 40-44 null Male
Sklearn Image / Computer Vision, LLMs, Tabular, Time Series / Forecasting, Classification AI Products Less than 3 months Amazon Web Services Gaps in tooling and support for model productionisation, Lack of specialised engineers, Building production-grade machine learning and data pipelines MLFlow null null Argo Workflows Amazon SageMaker FastAPI/Flask Wrapper Custom Built In-house tool Snowflake Amazon Bedrock Financial services 1,000-5,000 employees 10-20 21-100 Data Platform / Data Engineering Organisation Between 50% and 90% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 25-29 France Male
Sklearn Time Series / Forecasting Demand Forecasting, Pricing Less than a week Amazon Web Services Access to relevant data for training, Governance and domain risks Custom Built In-house tool null null Airflow Amazon SageMaker Sagemaker Custom Built In-house tool Custom Built In-house tool Anthropic Retail Less than 10 employees 2-5 21-100 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team Less than 10% CI/CD for continuous deployment, Development-Staging-Production Environments Data Scientist Manager 40-44 Germany Male
TensorFlow Image / Computer Vision, LLMs, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Recommender systems, Pricing Less than 3 months Azure Access to relevant data for training, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability Data Version Control (DVC) null Pinecone Airflow Azure ML Studio Databricks Custom Built In-house tool Azure DataLake null Technology 10-50 employees 10-20 10-20 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Less than 10% A/B Tests for Models, CI/CD for continuous deployment Business / Domain Practitioner Director/VP 40-44 Spain Male
Catboost Recommender Systems, Time Series / Forecasting Demand Forecasting, Recommender systems, Pricing More than a year Google Cloud Platform Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability MLFlow FEAST Weaviate Airflow Google Cloud Vertex AI Triton Evidently AI Snowflake Azure AI Technology 1,000-5,000 employees 10-20 2-5 A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation Between 10% and 30% A/B Tests for Models, CI/CD for continuous deployment MLOps Engineer Individual Contributor (Junior to Senior) 30-34 Ukraine Male
Sklearn Causal Inference, LLMs, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting Risk, Pricing, Automation More than a year Azure Access to relevant data for training, Lack of specialised engineers, Building production-grade machine learning and data pipelines MLFlow null null Databricks Databricks Databricks null Deltalake null Consultant 50-250 employees 10-20 21-100 Central Machine Learning Platform / Team Between 50% and 90% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer C-Suite 45-49 UK Male
Sklearn Causal Inference, LLMs, Tabular, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Fraud, Risk, Pricing Less than 3 months Amazon Web Services Machine learning monitoring and observability, Machine learning security, Governance and domain risks MLFlow FEAST PG-Vector Airflow Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Amazon Bedrock Financial services 1,000-5,000 employees 21-100 10-20 AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) More than 90% Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Manager 50-54 Brazil Male
Catboost Causal Inference, LLMs, Tabular, Text / NLP (Non-LLM) Fraud, Risk, Pricing Less than a year Azure Lack of specialised engineers, Lack of specialised data scientists, Building production-grade machine learning and data pipelines Custom Built In-house tool null null null null null null null null null null null null null null null null null null null null
PyTorch/Lightning/Fast.ai Causal Inference, Image / Computer Vision, LLMs, Recommender Systems, Search, Text / NLP (Non-LLM) Search, Recommender systems, Pricing Less than 3 months Azure Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Machine learning monitoring and observability MLFlow Custom Built In-house tool null Airflow Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Azure AI Retail 50,000+ employees 100-1000 100-1000 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Inventory (Keeping track of all AI usecases and models) Less than 10% A/B Tests for Models, Development-Staging-Production Environments Machine Learning Engineer Manager 50-54 UK Male
PyTorch/Lightning/Fast.ai Image / Computer Vision Embbeded systems Less than a month Google Cloud Platform Inconsistency of training and experimentation environments, Access to relevant data for training Weights & Biases null null null null FastAPI/Flask Wrapper null null Google Gemini Electronics Less than 10 employees 2-5 2-5 null Between 50% and 90% null Machine Learning Engineer Individual Contributor (Junior to Senior) 22-24 INDIA Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, Tabular, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Risk Less than 3 months Azure Inconsistency of training and experimentation environments, Access to relevant data for training, Lack of specialised engineers MLFlow Databricks null Airflow Databricks FastAPI/Flask Wrapper null Deltalake OpenAI Energy 5,000-50,000 employees 10-20 21-100 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Less than 10% null Machine Learning Engineer Individual Contributor (Junior to Senior) 30-34 Netherlands Male
Sklearn LLMs, Tabular, Text / NLP (Non-LLM) Marketing Intelligence, Fraud, Risk, Pricing Less than 6 months Azure Gaps in tooling and support for model productionisation, Lack of specialised engineers, Machine learning monitoring and observability MLFlow Custom Built In-house tool null Databricks Databricks Databricks null Snowflake Azure AI Financial services 1,000-5,000 employees 5-9 5-9 Data Platform / Data Engineering Organisation, AI Risk & Governance Function Less than 10% CI/CD for continuous deployment Data Scientist Individual Contributor (Staff+) 40-44 Norway Male
PyTorch/Lightning/Fast.ai LLMs, Recommender Systems, Search, Tabular, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Search, Recommender systems, Pricing Less than 3 months Azure Governance and domain risks MLFlow Custom Built In-house tool Milvus Argo Workflows Databricks Databricks Custom Built In-house tool GCP / BigLake Azure AI Retail 5,000-50,000 employees 21-100 21-100 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) Between 10% and 30% Canary Deployments, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 30-34 India Male
PyTorch/Lightning/Fast.ai Causal Inference, Image / Computer Vision, LLMs, Search, Text / NLP (Non-LLM) AI Products Less than 3 months Google Cloud Platform Inconsistency of training and experimentation environments, Building production-grade machine learning and data pipelines, Machine learning security null null ChromaDB null Custom Built In-house tool FastAPI/Flask Wrapper Tensorboard null Anthropic Technology 10-50 employees 5-9 10-20 Central Machine Learning Platform / Team Less than 10% CI/CD for continuous deployment Machine Learning Engineer Individual Contributor (Junior to Senior) 25-29 Pakistan Male
Sklearn Image / Computer Vision, Tabular, Time Series / Forecasting Demand Forecasting, electrical net design Less than a year Azure Gaps in tooling and support for model productionisation, Access to relevant data for training, Showcasing business impact and business value MLFlow null null Airflow Databricks Databricks Databricks Deltalake null Energy 5,000-50,000 employees 21-100 21-100 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Less than 10% CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 25-29 The Netherlands Male
Sklearn Image / Computer Vision, Tabular, Time Series / Forecasting Demand Forecasting Less than 6 months Azure Inconsistency of training and experimentation environments, Machine learning monitoring and observability, Governance and domain risks MLFlow Databricks null Airflow Databricks null null Azure DataLake Azure AI Energy 5,000-50,000 employees 21-100 100-1000 Central Machine Learning Platform / Team Less than 10% A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Junior to Senior) 25-29 Netherlands Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Tabular, Text / NLP (Non-LLM) Data Extraction Less than a month Amazon Web Services Gaps in tooling and support for model productionisation, Machine learning monitoring and observability Weights & Biases null Qdrant null Amazon SageMaker FastAPI/Flask Wrapper null AWS / Lakeformation Amazon Bedrock Technology 10-50 employees 10-20 10-20 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team Between 10% and 30% null Machine Learning Engineer Individual Contributor (Junior to Senior) 25-29 India Male
Sklearn Image / Computer Vision, Time Series / Forecasting Demand Forecasting, Fraud, Risk Less than 3 months Databricks Inconsistency of training and experimentation environments, Access to relevant data for training, Machine learning monitoring and observability MLFlow Databricks Databricks Airflow Databricks FastAPI/Flask Wrapper Custom Built In-house tool Deltalake null Energy 5,000-50,000 employees 21-100 100-1000 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) Less than 10% CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 25-29 The Netherlands Female
Sklearn Causal Inference, LLMs, Recommender Systems, Tabular, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Recommender systems Less than 3 months Amazon Web Services Inconsistency of training and experimentation environments, Access to relevant data for training, Showcasing business impact and business value MLFlow Databricks Databricks Databricks Databricks Custom Built In-house tool Databricks Deltalake Amazon Bedrock Retail 5,000-50,000 employees 10-20 21-100 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Less than 10% A/B Tests for Models, CI/CD for continuous deployment Machine Learning Engineer Director/VP 45-49 Denmark Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Recommender Systems, Tabular, Time Series / Forecasting Marketing Intelligence, Search, Recommender systems, Pricing, Logistics Less than 6 months Google Cloud Platform Building production-grade machine learning and data pipelines, Machine learning monitoring and observability Weights & Biases FEAST Milvus Kubeflow Google Cloud Vertex AI FastAPI/Flask Wrapper Custom Built In-house tool GCP / BigLake Google Gemini Technology 1,000-5,000 employees 5-9 10-20 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Between 50% and 90% Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments Machine Learning Engineer Individual Contributor (Junior to Senior) 25-29 Germany Male
PyTorch/Lightning/Fast.ai Causal Inference, LLMs, Recommender Systems, Search, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Search, Recommender systems, Fraud, Risk, Pricing, Product Less than 3 months Amazon Web Services Showcasing business impact and business value, Machine learning security, Governance and domain risks Weights & Biases Custom Built In-house tool Pgvector Airflow Amazon SageMaker Sagemaker Neptune AI AWS / Lakeformation All of the above Technology Less than 10 employees 2-5 5-9 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) Between 10% and 30% A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments Business / Domain Practitioner Individual Contributor (Staff+) 25-29 USA Male
XGBoost LLMs Data Extraction Less than a month Amazon Web Services Inconsistency of training and experimentation environments, Lack of specialised engineers, Machine learning monitoring and observability MLFlow null Qdrant Airflow Databricks Databricks Arize AI Snowflake OpenAI Financial services 1,000-5,000 employees 10-20 21-100 AI Risk & Governance Function Between 50% and 90% CI/CD for continuous deployment Data Scientist C-Suite 60+ USA Male
PyTorch/Lightning/Fast.ai Causal Inference, Image / Computer Vision, LLMs, Recommender Systems, Search, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Search, Recommender systems, Fraud, Risk, Pricing Less than 6 months Google Cloud Platform Building production-grade machine learning and data pipelines, Machine learning monitoring and observability, Governance and domain risks Weights & Biases Custom Built In-house tool Weaviate Airflow Azure ML Studio Sagemaker Neptune AI Azure DataLake Google Gemini Electronics Less than 10 employees 5-9 10-20 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation Between 10% and 30% Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment Machine Learning Engineer Individual Contributor (Junior to Senior) 30-34 India Male
Sklearn LLMs, Recommender Systems, Search, Text / NLP (Non-LLM) Search, Recommender systems Less than a month Azure Access to relevant data for training, Showcasing business impact and business value, Machine learning monitoring and observability MLFlow null null null Azure ML Studio null null null Azure AI Technology 50,000+ employees 1000+ 100-1000 A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) Between 50% and 90% A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments Data Scientist Individual Contributor (Junior to Senior) 25-29 uk Male
XGBoost Tabular, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Fraud, Risk Less than 3 months Azure Inconsistency of training and experimentation environments, Access to relevant data for training, Building production-grade machine learning and data pipelines MLFlow Databricks null Databricks Databricks Docker / Azure Functions Custom Built In-house tool Databricks null Financial services 50-250 employees 10-20 10-20 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function Between 10% and 30% Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Staff+) 30-34 Luxembourg Male
PyTorch/Lightning/Fast.ai Time Series / Forecasting Risk Less than a week Amazon Web Services Showcasing business impact and business value, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability MLFlow null null Airflow Lightning AI LitServe null GCP / BigLake Cursor Financial services Less than 10 employees 1 2-5 null Less than 10% Development-Staging-Production Environments MLOps Engineer Individual Contributor (Staff+) 40-44 United Kingdom Male
Lightgbm Tabular, Time Series / Forecasting Demand Forecasting Less than a month Hetzner Access to relevant data for training, Machine learning monitoring and observability null null null Celery null FastAPI/Flask Wrapper null null OpenAI Energy Less than 10 employees 1000+ 1000+ A Developer Productivity Team which also covers machine learning Less than 10% Development-Staging-Production Environments Business / Domain Practitioner Manager 30-34 Sweden Male
PyTorch/Lightning/Fast.ai Image / Computer Vision, LLMs, Recommender Systems, Tabular, Time Series / Forecasting Demand Forecasting, Risk, Pricing Less than 3 months Amazon Web Services Gaps in tooling and support for model productionisation, Lack of specialised data scientists, Machine learning monitoring and observability MLFlow Custom Built In-house tool Custom Built In-house tool AWS Step Functions Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Custom Built In-house tool Amazon Bedrock Hospitality 1,000-5,000 employees 5-9 10-20 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) Between 10% and 30% A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment Business / Domain Practitioner Director/VP 40-44 Germany Male
TensorFlow Image / Computer Vision, Time Series / Forecasting Demand Forecasting, Recommender systems More than a year Google Cloud Platform Gaps in tooling and support for model productionisation, Access to relevant data for training, Machine learning monitoring and observability MLFlow null null null null null null Custom Built In-house tool OpenAI Technology 250-1,000 employees null null A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation, AI Risk & Governance Function null null Machine Learning Engineer Manager 45-49 null Male
Sklearn LLMs, Recommender Systems, Tabular, Time Series / Forecasting Demand Forecasting, Marketing Intelligence, Fraud, Risk, Pricing Less than a year Azure Lack of specialised engineers, Lack of specialised data scientists, Building production-grade machine learning and data pipelines MLFlow azure ml's azure search null Azure ML Studio Azure ML Evidently AI Azure DataLake OpenAI Insurance 5,000-50,000 employees 5-9 21-100 Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Inventory (Keeping track of all AI usecases and models) Between 50% and 90% Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments MLOps Engineer Individual Contributor (Staff+) 35-39 nl null

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