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Issue #211
We wish to thank every single one of our 20k subscribers for your support πŸ’– and wish yo all happy holidays!
πŸ€“ and help us with a PR! πŸ€“
This #211 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 πŸš€
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This week in the MLE #211:
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!
As the year comes to an end, we look forward to reading the numerous & exciting year-in-review & tech predictions articles πŸ€“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 πŸš€ This is still a list in progress - please help us by adding a pull request with any relevant resources you find!
Reviewing computer science foundations seems to always lie within a standardised subset of data structures; linked lists, binary trees, hashmaps, stacks and the usual suspects. However as practitioners we benefit from learning more advanced concepts, and this article providers a great set of algos and data structure every programmer should try, including bloom filters, splay trees, topological sort and more.
Former Github VP Engineering, and current PlanetScale CEO Sam Lambert joins the "Dev Interrupted" Podcast in a thoroughly insightful conversation. In this episode he reflects about his time at GitHub, where he helped the then 40th most-trafficked website in the world run on just 2 servers, as well as his experience working at Facebook where he learned that you don’t need to sacrifice quality in order to move fast.
Learning and revewing the mathematical foundations for computer science & machine learning can be a huge booster for practitioner's core knowledge beyond the hype. This 2000+ page book seems to be the most thorough and complete resource covering the core foundations around Algebra, Topology, Differential Calculus, and
Optimization Theory for Computer Science and Machine Learning.
Explainability in machine learning is a hot topic, but one problem is that different methods for post hoc explanations have different goals, which can lead to a fragmented understanding and make it difficult to know which method to use. This research paper unifies eight popular post hoc explanation methods, showing that they all perform local function approximation of a black-box model. This not only advances our understanding of these methods, but also provides a guiding principle for choosing among them in practice.
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 ⭐ 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!
Β© 2018 The Institute for Ethical AI & Machine Learning