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Issue #245
This 245 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 45,000+  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 45,000+ 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!
Patterns for Building LLM-based Systems & Products is one of the most comprehensive deep dive articles on LLM-systems literature 💡 The article identifies key patterns and concepts related to LLM-application design. It covers topics such as evaluations to measure performance, Retrieval-Augmented Generation to enhance LLM outputs with external data, and fine-tuning to tailor pre-trained models for specific tasks. Additionally, caching is highlighted as a method to store and quickly access previously computed data, reducing latency and costs. The content provides a broad range of academic research, industry resources, and practitioner expertise, underscoring the need for thoughtful system design based on user request patterns.
Facebook/Meta has unveiled "Code Llama", a new specialized version of their Llama 2 model tailored for coding tasks which works out-of-the-box with Llama.cpp. This model can generate code, assist with code completion, and support debugging across several languages, including Python and Java. The relase includes three model sizes (7B, 13B, and 34B parameters) which provide differnet levels of quality and computational overhead. Code Llama has outperformed other code-specific LLMs in benchmarks which is great news for an open-source-ish (as the license is still limited on permissions) model.
"An Elegant Puzzle" by Will Larson is one of the best books on management of highly technical teams. For anyone that has had the change to read the book this is a fantastic resource to provide a set of key points for robust resources (if you haven't I certainly recommend it). This book delves into technical people management, emphasizing systems thinking, organizational structures, and career progression. It underscores the significance of mentorship, managing technical debt, data-driven decision-making, and effective time management. The book also touches upon the nuances of hiring and onboarding. These insights are particularly relevant for production ML practitioners, guiding them in understanding their role within larger systems and optimizing their workflows.
Hands on crash-course on end-to-end MLOps 🤖 This repository provides MLOps resources to build a Serverless ML-powered API to predict crypto prices showcasing popular MLOps tools and frameworks. It guides users through model training, deploying the model as a REST API, and automating deployments using GitHub Actions and the Model Registry. This tutorial leverages various tools lincluding CometML, Cerebrium, and GitHub Actions.
Probabilistic Machine Learning: Advanced Topics - a fantastic free book to dive into the advanced topics of probabilistic machine learning. Bridging foundational concepts with cutting-edge techniques, this book delves into topics from inference methods to generative models, seamlessly integrating classic teachings with modern advancements like denoising diffusion models. This resource has contributions from leading figures in the machine learning community including Google, Deepmind, UBC, Stanford, and many others.
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.
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.
Check out our website
© 2018 The Institute for Ethical AI & Machine Learning