The MachineLearning EngineerIssue #143

This week in Issue #143:

• Machine Learning EngSci Book
• Graph Deep Learning Overview
• Massively Scaling ML Training
• FAANG Interview Prep Repository
• Awesome Kubernetes Lists
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more 🚀

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If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone’s support!

Machine Learning EngSci Book

A new online machine learning text book targetted for engineers and scientists being created from a recent online course, and which is covers a wide variety of resources including the underlying foundations, techniques, user aspects and matters related to ethics an responsibility in AI. Until the book is out the online course is available for free, which is yet another great resource that practitioners can benefit from.

Graph Deep Learning Overview

This article aims to provide an intuitive introduction and overview of graph deep learning, and covering the concepts for implementing the internals as well as how it all integrates together.

Massively Scaling ML Training

Very interesting paper that discusses the motivations, challenges and solutions for massive-scale model training, and proposes a distributed training framework for giant models with the codename Whale.

FAANG Interview Prep Repository

For practitioners looking to brush up their data structures and algorithms, here is a github repo that contains a very comprehensive list of resources and material for typical algorithmic interview questions.

Awesome Kubernetes Lists

Similar to other “awesome” github lists, this “awesome kubernetes” list provides a set of useful resources for practitioners to dive into the world of kubernetes, and for intermediate practitioners to brush up their foundational skills.

OSS: GPU Accel. Frameworks

The topic for this week’s featured production machine learning libraries is GPU Acceleration Frameworks. 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. The four featured libraries this week are:

• Vulkan 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 libraries that are not in the “Awesome MLOps” list, please do give us a heads up or feel free to add a pull request!

OSS: Awesome AI Guidelines

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:

• 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
• From What to How - An initial review of publicly available AI Ethics Tools, Methods and Research to translate principles into practices

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!