Subscribe to the Machine Learning Engineer Newsletter

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

Issue #238
This 238 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 40,000+  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 40,000+ 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!
Evidently AI created a fantastic resource containing 200 real-word case studies of ML Systems from 64 companies including Netflix, Airbnb, and Doordash. Each of these contain further resources that cover the ML use cases in more detail and provide insights from the design of ML systems. This airtable database allows for filtering by industry or ML use case, and tags are added based on recurring themes. The most popular use cases are recommender systems, search and ranking, and fraud detection. The database also highlights instances where ML powers a specific user-facing "product feature", such as grammatical error correction or generating outfit combinations.
A great article from Outerbounds, the company behind Metaflow, which explores instruction tuning for large language models. Here they dive into customisations to enable these models to generate appropriate responses to specific instructions. The authors discuss the rise of open-source LLMs, the role of commercial entities in the field, and the challenges of applying LLMs, including hardware access, real-world application, and ethical issues. Instruction tuning is presented as a valuable tool for fine-tuning LLMs, offering developers greater control over LLM behavior and enabling the creation of unique, functional product experiences.
New free online course on "Large Language Models: Application through Production" 💡 A great resource for developers, data scientists, and engineers who aim to build applications powered by large language models. The course covers the application of LLMs on real-world natural language processing problems, the integration of domain knowledge into LLM pipelines, the nuances of pre-training and fine-tuning models, and the implementation of LLMOps best practices. It also addresses the societal, safety, and ethical considerations of using LLMs. By the end of the course, participants will have built an end-to-end LLM workflow ready for production.
Meta / Facebook has released their "approach to explaining (ML) ranking" 💡 This resource provides a set of deep dives that contribute to their attempt to provide transparency on their AI-powered algorithms across their platform; this includes how posts are ranked in newsfeed, recommendations, ranking of comments, friend recommendations, notifications and more. This resource also covers some of their perspective towards battling misinformation, detailing how they work with independent fact-checkers to identify and take action on misinformation.
Fantastic episode of"Talk Python Podcast" on"Python at Netflix" featured two guests from Netflix's Python Infrastructure team, Amjith Ramanujam and Srinivasan Ramanujam. In this podcast they dive into the extensive use of Python across various teams at Netflix, including security, machine learning, data science, and animation studios. They also touched on tools like Security Monkey and Portable Python, and how they support different teams by building personas for various Python use cases.
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