Excited to release initial insights for our Survey on Production MLOps!! The survey would still benefit from your contribution and it's OPEN FOR RESPONSES 🚀🚀🚀
About 40% of organisations do NOT have monitoring in their Machine Learning (+10% YoY)! On ML Monitoring tooling, 20% is custom / in-house (-7% YoY) and almost 20% EvidentlyAI (+7% YoY)! |
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DORA AI-Dev Report 2025 The 2025 DORA Report on the State of AI-assisted
Software Development is out! Here are a few important highlights for any tech leaders in the space: 1) Code quality: 39% show no impact or worsened; 50% slight to moderate improvement. 2) Productivity: 39% show no impact or worsened; 50% slight to moderate improvement. 3) Trust in AI output: 30% little/none; 24% a lot/great deal. 4) AI use at work: 90% use (+14 YoY). 5) Agent mode: 61% never; 39% at least occasionally. 6) Platform engineering: 90% org adoption; 76% have dedicated platform teams. It is clear that as of today the adoption of AI for development is not "whether" but "how" and "how much"; we can expect a lot of insights to come out throughout the next few months/years in the topic! |
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Chip Huyen on Lenny's Podcast I definitely recommend checking out last week's edition of Lenny with Chip Huyen where they cover all-things AI Engineering! There was really great (foundational) advice throughout: 1. Talk to users; improve data; fix workflows; iterate prompts/UX—don’t chase shiny tools. 2. Pre-training gives capability; post-training shapes behavior. 3) Treat fine-tuning as a last resort after RAG, prompting, and system fixes. 4) Use RLHF/AI-feedback and verifiable rewards to steer models. 5) RAG quality is mostly a data-prep problem (chunking, metadata, QA reformats, synthetic Qs). 6) Write targeted evals for core user journeys and each step of multi-hop workflows. 7) Measure coding-tool impact with controlled trials and business metrics—not LOC. 8) Expect biggest gains among already high performers. 9) Many "AI problems" are UX/reliability issues (latency, voice turn-taking, disclosure). As always Chip has some really great takes, and they are basically reminders to not forget the basics / foundations; definitely a great podcast worth checking out! |
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The State of MLOps 2025 Survey 🔥 We are excited to release initial insights for our Survey on Production MLOps!! The survey would still benefit from your contribution and it's OPEN FOR RESPONSES 🚀🚀🚀 About 40% of organisations do NOT have monitoring in their Machine Learning (+10% YoY)! On ML Monitoring tooling, 20% is custom / in-house (-7% YoY) and almost 20% EvidentlyAI (+7% YoY)! We are still continuing to gather the insights on this year's MLOps Survey! We still need your support to continue collecting diverse perspectives to map the ecosystem! Please help us with your response, as well as by sharing with your colleagues 🚀🚀🚀 If you have a few minutes, your contribution will make a significant difference to the whole production ML ecosystem 🥳 The results will be shared as open source like last year!! You can add your response directly at: bit.ly/state-of-ml-2025 🔥 |
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Europe's EuroLLM Launches A few of Europe's top universities have launched EuroLLM, a 9B-parameter open-weight LLM which supports the 24 official EU languages! It's great to see some of these exciting initiatives, particularly when the artifacts are released as open-source / open-weights! From the technical paper it seems it was trained from scratch on 4T tokens using 400 H100s on EuroHPC’s MareNostrum 5, with a dense 42-layer Transformer (GQA, RoPE, 4k context) and an instruction-tuned variant for production use. It's also interesting to see the strong multilingual performance which was compared with Gemma-2-9B and other open models on aggregate benchmarks. It goes without saying but it is great to see the innovation projects that Horizon Europe funding is enabling; this is certainly a key important requirement for driving innovation across Europe. |
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Learning PyTorch the Hard Way This is a fantastic example of "learning PyTorch the Hard Way", but often one of the best ways which is by diving into the internals after something doesn't work as expected: This is basically an interesting situation of strange-user-behaviour turns into obscure-intricate-bug, and this is quite a great walk-through on some of the PyTorch internals as well. At least for me, going into the internals of a particular framework helps me build a stronger foundational knowledge not only on the tooling but also on the domain itself. This is one of the reasons why they suggest open source contributions is a great way to develop stronger technical skills. Check out the deep dive!
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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:
Other upcoming MLOps conferences in 2025:
In case you missed our talks:
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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! |
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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 takle 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 vulnerabiliites 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.
If you know of any guidelines that are not in the "Awesome AI Guidelines" list, please do give us a heads up or feel free to add a pull request!
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| | | | The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning. | | | | |
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