The MachineLearning EngineerIssue #88

This week in Issue #88:

• FastAI New Course, Libs & Book
• Massive Scaling Google Meets
• DeepFakes Threat Report
• AI Developer Tools Landscape
• Beginner to Professional Dev with Python
• Featured OSS Production ML Libraries
• 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!

FastAI New Course, Libs & Book

FastAI has announced a massive release - they have just made available their FastAI V2 framework, they released multiple new libraries (fastcore, fastscript and fastgpu), they have released an online course on deep learning for coders, and a brand new book from O’Reilly that delves deeper into these concepts.

Massive Scaling Google Meets

The Google Meets team provides an overview of how they tackled the massive spikes in use on the Google Meets platform. In this post they cover how they dealt with the massive spikes in traffic and usage since the increase of remote working increased due to the COVID pandemic. They delve into the key strategies they took to ensure operational sustainability, as well as the results achieved.

DeepFakes Threat Report

The rise of deepfakes could enhance the effectiveness of disinformation efforts by states, political parties and adversarial actors. This report offers a comprehensive deepfake threat assessment grounded in the latest machine learning research on generative models, and delves into how rapidly is this technology advancing, as well as who in reality might adopt it for malicious ends.

AI Developer Tools Landscape

Databricks Director of Product Clemens Mewald has put together an overview of the AI Developer Tools landscape for enterprises. In this post he covers the dominant design in ML APIs & Platforms, together with some of the key challenges in the ecosystem.

Beginner to Prof. Dev with Python

Capital Group Principal Engineer Joel Grus joins this week’s Data Exchange podcast. In this session Joel talks about his new book “Ten Essays on Fizz Buzz”, key concepts in hiring software engineers, as well as key data science & ML tools for engineers.

OSS: Model Serving Framework

The topic for this week’s featured production machine learning libraries is Model Serving 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:

• KFServing - Serverless framework to deploy machine learning models in Kubernetes with KNative
• Seldon Core - Open source platform for deploying and monitoring models in kubernetes with rich DAG structures
• Cortex - Cortex is an open source platform for deploying machine learning models—trained with nearly any framework—as production web services.
• Tensorflow Serving - High-performant framework to serve Tensorflow models via grpc protocol able to handle 100k requests per second per core

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 thiese 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. We will be showcasingitg three resources from our list so we can check them out every week. This week’s resources are:

• 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!

About us

The Institute for Ethical AI & Machine Learning is a Europe-based research centre that carries out world-class research into responsible machine learning.