Survey on Production MLOps

The results for the Survey on Production MLOps are out: ethical.institute/state-of-ml-2025 🚀🚀🚀 As part of the release we have updated the interface enabling real time toggling between 2024 and 2025 data, and have refreshed a cool new code-editor theme 😎 Check it out and share it around!!

Further insights:

In 2025, the top 5 challenges organisations face in Production ML are:

  • ML System Monitoring 16% (+2% YoY)
  • Access to Data 14% (+2% YoY)
  • Data & ML Pipelines 13% (+2% YoY)
  • Training/Experimentation Env Parity 12% (+1% YoY)
  • ML Governance 8% (+2% YoY)
  • Security 8% (+6% YoY)

It is super interesting to see the big jump on ML Security from 2% all the way to 8%, as well as the continued increase of ML Monitoring as the top 1 challenge, which reflects the awareness and maturity of organisations in understanding the implications and risks of production ML systems. It is also interesting to see how last year Gaps in ML Tooling was the top 2 on the list with 12% and this year it's down to 8% completely out of the top 5 which reflects how choice is no longer becoming the blocker, and instead it's the cohesive integration and robust productionisation. Finally it seems that also Engineering Talent is slowly declining with 8% this year vs 10% in 2024, which again shows that the skill gap is slowly closing. The top 5 challenges in production ML in 2025 do seem to reflect some of the qualitative trends that I see on our day-to-day. If you want to dive deeper you can access the full results here: https://ethical.institute/state-of-ml-2025

This week in ML Engineering:

Upcoming MLOps Events

The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below. Upcoming conferences where we're speaking:

Open Source MLOps Tools

Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ⭐ github stars. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. Four featured libraries in the GPU acceleration space are outlined below:

  • Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced data processing usecases.
  • CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it.
  • Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
  • CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
    If you know of any open source and open community events that are not listed do give us a heads up so we can add them!

OSS: Policy & Guidelines

As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to tackle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following:

  • MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabilities in the machine learning lifecycle as well as best practices.
  • AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems.
  • An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles.
  • ACM's Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018.

About Us

The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning.