The MachineLearning EngineerIssue #173

This #173 edition of the ML Engineer newsletter contains curated articles, tutorials and blog posts from experienced Machine Learning and MLOps professionals. 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 on 🐦 Twitter, 💼 Linkedin and 📕 Facebook!

This week in Issue #173:

• Linkedin’s Explainable AI RecSys
• Google’s AI Autogen Summary
• OpenAI’s Text to Image Model
• Wisdom from 50+ Years of Code
• Continuous Intelligence at Scale
• Open Source ML Frameworks
• Awesome AI Guidelines to check out this week
• + more 🚀

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!

Linkedin’s Explainable AI RecSys

A really insightful article showcasing how they approached the design, development and productionisation of an explainable AI recommendation system to help them scale sales efficiency across the Linkedin organisation.

Google’s AI Autogen Summary

The Google AI team explains how they tackled text summarisation for Google docs using machine learning, discussing key insights from the data, model training and even MLOps infrastructure for serving to productionise these machine learning capabilities.

OpenAI’s Text to Image Model

The team at OpenAI launches the updated version of their DALL-E model which generates realistic images from text - this has sparked fascinated examples with creative portraits of animals, dinosaurs and more random creative images.

Wisdom from 50+ Years of Code

An interesting with software development pioneer Brian Kerninghan, who shares great insights learned throughout his 50+ years of programming since the early days of Unix in Bell Labs, covering programming languages like C, Rust, Go and beyond.

Continuous Intelligence at Scale

The Data Exchange podcast comes back with another great conversation on distributed data infrastructure for analysis, learning and predictions in real time.

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:

• 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!