Andrew Ng New Stanford ML Course Stop everything you are doing; THE course on ML by Andrew Ng at Stanford is getting a revamp, and is being published as we speak. This was one of my first introductions to machine learning; the only point I will miss is the blackboard and chalk as it seems even that is being modernized! Personally I will be looking to book some time off to block out enough time to properly go through it to touch back once again into some of the core foundations. It is such a lucky time to be alive such that content of such high quality is made available for free, huge kudos to Stanford and Andrew Ng for publishing these resources, as they will inspire and enable the next (and past) generations of ML practitioners. |
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Introduction to Multi-Armed Bandits Multi-armed bandits are a powerful framework that enable you to make decisions over time under uncertainty, and this paper from Microsoft is one of the best advanced introductions. The literature on MABs is quite extensive and continues to grow across the years, but this book provides a comprehensive coverage across all the topics. This resource dives on the foundations including KL-divergence, bayesian bandits & thomson sampling, similarity information, adversarial bandits, contextual bandits, and various advanced topics such as bandits with agents. Coming across resources like these make me realise how little I know about a topic by coming across the depth of the concept and applications. |
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Designing Agentic Loops Simon Willison has put together a fantastic overview on how to design agentic loops, which seems to be a seemingly growing skill for developers to at least have an intuition towards. Agents like Claude Code or Codex CLI are implementing in one way or another such an agentic coding loop, which provides structured, iterative workflows where LLMs autonomously run tools to reach a goal. Balancing safety and flexibility seems to be a continuous tradeoff - e.g. enabling YOLO mode make it feel more productive, but requires stricter sandboxing. Effective loop design seems to involve concepts that are still being defined, however there seems to be a growing number of concepts that are starting to solidfy as best practice. |
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The State of MLOps 2025 Survey 🔥 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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Building Generative Recommendations One of the best ways to learn a concept is by going through the implementation; this is a great opportunity to understand recommender systems through practice: In this case the approach is by through the "OneRec" generative recommender framework which was originally performing video recommendations, but adapting it to friend recommendations. In friend recommendations, positive signals are far sparser and more delayed, which introduces further considerations that we'd have to also understand. In practice the architecture remains the same, but there are key modifications required on the design of the embeddings and training objectives. This is quite an interesting approach that leverages LLM infrastructure to unify recommendation stages - and more importantly this is a great way to really understand some of the underlying concepts. |
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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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