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Issue #212
This #212 edition of the ML Engineer newsletter contains curated ML tutorials, OSS tools and AI events for our 15,000+  subscribers. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions ๐Ÿš€
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This week in the MLE #212:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
Production machine learning use-cases continue to grow in the gaming industry ๐ŸŽฎ Ubisoft has put together an insightful overview of their journey developing their end to end machine learning systems inspired from Uber's Michelangelo - they cover design, components, use-cases and beyond.
Measuring engineering success can be challenging and ambiguous. Will Lethain, author of "An Elegant Puzzle" and "Staff Engineer" has put together an insightful article exploring effective ways on measuring engineering teams and organizations. He proposes a template for identifying some initial metrics that can provide meaningful value, as well as approaches to expand further.
As 2023 begins we look forward to reading the numerous & exciting year-in-review & tech predictions articles ๐Ÿค“However instead of filling this newsletter with these, we have put together a comprehensive list of "Awesome Annual Tech Reviews & Predictions", containinga long list of year-in-review & tech predictions articles for 2022-2023. This list covers resources from open source projects, tech companies, thought leaders and NGOs ranging across AI, Data, GameDev, General Tech, Security and beyond ๐Ÿš€ This is still a list in progress - please help us by adding a pull request with any relevant resources you find!
A Succinct Summary of Reinforcement Learning. This document is a concise summary of many key results in single-agent reinforcement learning. The intended audience are those who already have some familiarity with RL and are looking to review, reference and/or remind themselves of important ideas in the field.
FastAPI is continuously adopted for productionisation of machine learning powered applications. This resource provides an opinionated list of best practices and conventions. It covers project structure, formatting, tools, frameworks, documentation and much more.
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.
Conferences we'll be speaking at:
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 โญ 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!
ยฉ 2018 The Institute for Ethical AI & Machine Learning