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Issue #227
This 227 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 30,000+  subscribers. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions 🚀
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This week in the ML Engineer:
Thank you for being part of over 30,000 ML professionals and enthusiasts who receive weekly articles & tutorials on production ML & MLOps 🤖 If you havent, you can join for free at
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
Exciting new resource for the Machine Learning community from MIT on Foundation models 🤖 MIT has released a free course on "Self-Supervised Learning & Foundation Models". This comprehensive program covers state-of-the-art topics and techniques. Topics covered include ChatGPT, Stable-Diffusion & Dall-E, Neural Networks, Supervised Learning, Representation & Unsupervised Learning, Reinforcement Learning, Generative AI, Self-Supervised Learning, Foundation Models, GANs, Contrastive Learning, Denoising & Diffusion Auto-encoders. A fantastic resource from MIT's renowned experts and dive deep into the fascinating world of AI and machine learning.
The MLOps Bookshelf Collection 📚 A great resource for practitioners looking to upgrade their knowledge in production machine learning operations. The list includes books such as "Building Machine Learning Powered Applications" by Emmanuel Ameisen, "Reliable Machine Learning" by Cathy Chen et al., "Machine Learning Engineering in Action" by Ben Wilson, "Effective Data Science Infrastructure" by Ville Tuulos, "Machine Learning Design Patterns" by Valliappa Lakshmanan, and "Designing Machine Learning Systems" by Chip Huyen. The article also provides brief reviews and recommendations for each book.
The team behind one the Open-Assistant project making available an Open Source version of ChatGPT has put together a fantastic compilation of research papers on conversational LLMs 💡  The article provides a list of research papers relevant to production machine learning practitioners. The papers cover various topics such as reinforcement learning from human feedback, generating text from language models, automatically generating instruction data for training, uncertainty estimation of language model outputs, evidence-guided text generation, reward model optimization, dialogue-oriented RLHF, and reducing harms in language models. The papers discuss various methods for fine-tuning language models, improving the quality of generated text, reducing the need for manually annotated data, teaching models to express their uncertainty, and reducing harms caused by language models.
Using LLMs to understand large-scale complex codebases 🤯 This article demonstrates how LangChain, Deep Lake, and GPT-4 revolutionize code comprehension, enabling developers to understand complex codebases like Twitter's recommendation algorithm effectively and efficiently. The new method involves four key steps: indexing the codebase, storing embeddings and code in Deep Lake, using LangChain's Conversational Retriever Chain, and asking questions in natural language. The result is a faster, more efficient code understanding process that streamlines learning for machine learning practitioners.
RocksDB Under The Hood
RocksDB Under The Hood 🛠️ RocksDB is an embeddable persistent key-value store that has gained popularity in recent years, with its adoption by major tech companies like Meta, Microsoft, Netflix, and Uber. This article provides a very insightful and thorough analysis of the internals, together with the concepts, theory and examples of actions such as flushes, merges, etc.
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.
Other relevant upcoming MLOps conferences:
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!
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!
About us
The Institute for Ethical AI & Machine Learning is a UK-based research centre that carries out world-class research into responsible machine learning.
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© 2018 The Institute for Ethical AI & Machine Learning