The MachineLearning EngineerIssue #144

This week we celebrate fantastic milestones 🚀

• The MLE Newsletter has reached 6000+ subcribers 🥳
• The Production ML List reached 10,000+ Stars 🌟
• The Kompute Framework reached 500+ Stars 🎁
• The AI Guidelines List reached 600+ Stars 🎈

We would like to thank all of our subscribers including YOU 🎈 here is to many more celebrations to come 🚀

This week in Issue #144:

• Kompute v0.8.0 with CNNs, Edge & Optimizations
• ML the High Interest Credit Card of Tech Debt
• Gentle Intro to Graph Neural Nets
• Hands on Educational ML with EpyNN
• Adoption Story of GraphQL at Paypal
• 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!

Kompute v0.8.0 Now Released

We are thrilled to announce the v0.8.0 Release of the Kompute Project, which continues to work towards advancing the cross-vendor GPU acceleration ecosystem. This release includes several achievements including the 500 Github Star Milestone, Edge-Device Support, CNN Implementations, Variable Types, MatMul Benchmarks, Binary Optimisations and more 🚀

ML and High Interest Tech Debt

A fascinating paper by Google researchers that provides an in-depth intuition on the dangerous impacts of “quick wins” in long term machine learning technical debt.

Gentle Intro to Graph Neural Nets

This article provides an intuition on why and how neural networks have been adapted to leverage the structure and properties of graphs. It explores the components needed for building a graph neural network - and provide the motivations the design choices behind them.

Educational ML with EpyNN

An interesting project that aims to provide scalable, minimalistic and homogeneous implementations of major Neural Network architectures in pure Python/Numpy with the main purpose to serve as an educational tool that can be adopted for building a practical intuition on foundational key concepts in deep learning

Adoption of GraphQL at Paypal

Paypal shares their experience throughout their journey adopting GraphQL, including their motivations, initial steps, scaling best practices and tangible results.

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!