Chip Huyen on AI Engineering Join AI founder & Author of O'Reilly Book "Designing ML Systems" Chip Huyen at this upcoming ACM fireside chat discussing practical advice on "AI Engineering" moderated by IEML Founder and Zalando Director Alejandro Saucedo. This session will explore the unique challenges of productionizing foundation models compared to traditional machine learning models. Despite sharing some core principles, foundation models introduce new complexities due to their open-ended nature, advanced capabilities, and computational demands. Key changes include shifting from closed-ended to open-ended evaluation, from feature engineering to context construction, and from structured data to unstructured data. This will be a great session so don't forget to RSVP! |
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How META Trains LLMs at Scale META recently made a massive investment to train large language models at scale - they now have shared a great overview of their learnings and challenges. Some of the key innovations that enabled META to succeed (so far) have included optimizing GPU connectivity, leveraging advanced scheduling algorithms, and adapting high-performance hardware. Meta has implemented robust network infrastructures that showcase the nuance required to support massive GPU clusters - this is an endevour that will only grow more ubiquitously across tech companies fighting across the AI race. |
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AI Search The Bitter Lesson The Bitter-er Lesson of AI search: opportunities integrating search capabilities with foundation models could revolutionize AI research and scaling laws. A great introspective analysis of search engines in context of the race between AI models and search. As history repeats, there is growing opportunity combining the power of human-crafted heuristics with advanced search capabilities, which can and is already unlocking new approaches for accelerating AI advancements. |
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Uncensoring LLM Research The growing cybersecurity race in LLMs between safety mechanisms to limit undesirable content, and techniques to exploit these guardrails - this resource covers a great practical intution on how to uncensor any LLM using "abliteration", namely a method to uncensor LLMs by removing their built-in refusal mechanisms without retraining. This technique involves identifying a specific "refusal direction" in the model's residual streams and either subtracting it during inference or modifying model weights to prevent its representation - by collecting activations from harmful and harmless prompts and computing the mean difference, practitioners can apply this technique to enable the model to respond to all prompts. Experiments like such put into perspective the challenges in technical safety considerations, highlighting the importance of security in context of agentic applications. |
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Apple AI Private Cloud Compute Apple's Private Cloud Compute: A new frontier for AI privacy in the cloud. A great insight into the shift in persepctive from consumers towards AI, highilighting the growing role of privacy and security. Apple presents key challenges in security and privacy in relation to data in context of AI systems, as well as their approach to improve the consumer sentiment. Some of these features include things like custom Apple silicon processors, "privacy-focused OS" which is stateless, with ephemeral data processing, with no privileged access, making user data inaccessible to anyone, including Apple. Ultimately the secure, private and safe processing of AI systems will require considerations that go well beyond that of traditional software, and certainly will be a space worth keeping an eye. |
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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 2024:
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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