Subscribe to the Machine Learning Engineer Newsletter

Receive curated articles, tutorials and blog posts from experienced Machine Learning professionals.

Issue #244
This 244 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 🚀
If you like the content please support the newsletter by sharing with your friends via 🐦 Twitter,  💼 Linkedin and  📕 Facebook!
 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!
Stanford a new online course available to everyone for free on Natural Language Understanding 💡 This course offers a deep dive into the mechanics of NLU, blending theoretical insights from linguistics, NLP, and machine learning. The curriculum is delives into foundational topics like contextual language representation and information retrieval, followed by hands-on model building and an original NLU project. Tailored for real-world application, the course emphasizes best practices, ensuring participants are equipped to deploy robust NLU systems in production environments.
OOD Detection represents an emerging trend in deep learning research, focusing on a critical deficiency that often limits the deployment of neural networks in real-world scenarios 🤖 This Awesome Out-Of-Distribution Detection repository contains a comprehensive list of research papers, code implementations, insightful blogs, datasets, and tutorials. This repository is an invaluable resource for production machine learning practitioners aiming to enhance model robustness and stay updated on the latest OOD detection techniques and methodologies.
Vector databses are have been taking the world by storm, and this 4-part guide provides a comprehensive introduction and deep dive 🤖 This article provides specifically guidelines on selecting the right vector database solution. It emphasizes the trade-offs between on-premises vs. cloud hosting, client-server vs. embedded architectures, and the benefits of purpose-built vector DBs over incumbent solutions. The piece also delves into performance metrics, storage considerations, vector types, hybrid search strategies, and filtering techniques. Conclusively, while there's no universal solution, Rust-built databases like Qdrant and LanceDB, with their developer-centric approach, are highlighted as promising options in the vector DB landscape.
An interesting article written back in March trying to answer how it can run in a single CPU. The article explores the feasibility of running the LLaMa inference code in raw C++ on various devices, from smartphones to laptops and Raspberry Pis. It emphasizes that while GPUs are traditionally preferred for deep learning due to their vast memory bandwidth and compute capabilities, memory bandwidth often becomes the bottleneck for inference. By employing techniques like quantization, which reduces precision, the memory requirements of models can be significantly decreased, enabling them to run on devices with limited resources. The article provides specific performance calculations for different devices, underscoring the importance of memory bandwidth in transformer model sampling.
A plain english introduction to CAP Theorem 💡 For practitioners working with production systems, understanding the CAP theorem is crucial as it informs the trade-offs in data consistency, system availability, and fault tolerance. The article uses an intuitive example to simplify the understand the CAP theorem, which posits that it's impossible for a system to simultaneously achieve Consistency (every read gets the most recent write), Availability (every request gets a response), and Partition Tolerance (the system operates despite network failures). As the venture faces challenges, the article demonstrates these concepts, emphasizing that while two of the three properties can be optimized, all three can't be achieved together. An additional section introduces "eventual consistency", where updates occur in the background, sacrificing immediate consistency for improved availability and fault tolerance.
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!
© 2018 The Institute for Ethical AI & Machine Learning