International AI Safety Report The first International AI Safety Report was chaired by Yoshua Bengio and developed by a diverse group of 96 AI and an Expert Advisory Panel: Although there are no major surprises in the report, this is a great overview of the concepts, taxonomies and best practices in AI Safety, standardising risks associated with deploying general-purpose AI models. Some of the categorisation of malicious use-cases encompass deepfakes, cyberattacks, and dual-use applications (e.g., bioweapon design), as well as malfunctions such as hallucinations, bias, and loss of control. This is a fantastic resource, and it's really great to see the steps forward in this space - check it out. |
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The DeepSeek Illustrated Guide As the hype for the DeepSeek-R1 continues to grow, we have been able to see few deep dives that go beyond the hype; this is a great visual guide to understand the foundations of this new model. What better way to understand an emerging context than with an illustrated guide which covers DeepSeek-R1's model architecture, multi-stage process, pre-trained base, fine-tuning approach on 600,000 chain-of-thought examples, and its reinforcement learning approaach R1-Zero. |
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AI Engineering Pitfalls w Chip Huyen Chip Huyen dives into a practical roadmap for modern AI engineering on the TWIML AI Podcast! This is a great overview for production ML practitioners with an interest to blend classical ML with generative models. The topics discussed cover relevant areas of AI engineering such as prompt engineering, agent design, open-source models, synthetic data generation, and evolving compute challenges. This is certainly a while podcast to check out to get up to speed with the foundations of AI Engineering. |
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CMU ML In Production Course Carnegie Mellon University has just dropped a no-nonsense in-depth course on production machine learning systems, and I have to say it's one of the most comprehensive resources I've seen: It is great to see CMU covering a course syllabus on production ML that covers some of the key pressing topics in the MLOps ecosystem, really diving into even the foundations of both real time and batch ML. The course content and slides really go broad covering best practices designing systems that manage prediction errors, safety, fairness, and scalability, to deploying, testing, and monitoring models using tools like Docker, Apache Kafka, Jenkins, and Kubernetes. It is unfortunate at least on some of the slides it suggests that the course is not recorded, but hopefully the content for this or a similar resource is published - for the time being, the PDF slides and repo do seem comprehensive enough! |
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No-Hype DeepSeek Reading List If you want to go beyond the hype of the DeepSeek-R1 new model, this is great reading list that covers some really interesting papers and resources for ML practitioners: This is really quite a great curated list on DeepSeek's R1 model, covering some of the fundamental research underpinning DeepSeek-R1 including Transformer architectures, Chain-of-Thought reasoning, Mixture of Experts, and Reinforcement Learning in the context of LLMs (RLAIF vs. RLHF, Self-Rewarding LMs, Thinking LLMs, DPO). |
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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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