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Issue #76
Fantastic updates this week 🚀 We have reached over 3000 subscribers, The Institute's Chief Scientist has been appointed to ACM Council as Member-at-Large, announcing our opening keynote at EuroPython 2020, and our open source Production Machine Learning list has reached 3500 stars 🔥🔥🔥 a massive thank you to all our community members for all your support ✨👏🎉😃 #LetsKeepDoingThis
This week in Issue #76:
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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! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
Highlights for this week at The Institute for Ethical AI, Chief Scientist Alejandro Saucedo has been appointed to the Association for Computing Machinery's Governing Council as Member-at-Large for a 4-year term.  Additionally we're announcing our opening keynote at this year's EuroPython 2020 conference on responsible development of production systems titled "Meditations on First Deployment: A Practical Guide to Responsible Development".
"ML in Production" is a website that curates content focused around best practices for building real world machine learning systems. They have put together a fantastic five-part series that dives into the concepts and challenges of production machine learning, including the definitions, the software interfaces, batch processing, online inference and ml deployment.
Navigating the wide and deep range of machine learning tools can be hard, especially for fast-moving requirements that startups face. In this article 41 machine learning startups were surveyed across the world to gain understanding on the tools, libraries and frameworks used on a day to day basis. The insights obtained are grouped into Methodology, Software Development setup, ML Frameworks, MLOps and "the unexpected".
A paper was released last week covering initial achievements in the experimental results the GPT Language Model, trained on almost 500 Billion tokens and 175 Billion parameters. This 60 minute video dives into the paper and breaks it down in an intuitive and comprehensible perspective, covering the terminology & foundations, details on the model size & dataset, methodology, fine tuning, experimental results and much more.
Machine Learning Mastery has put together a comprehensive article which dives into how to use robust scaler transforms to standardise numerical input variables for classification and regression. In this tutorial they cover the algorithms that benefit from these techniques, some of the approches that enable it, and how to use the RobustScaler to scale numerical input variables using the median and interquartile range.
[Updated List 31/05/2020] Due to the current global situation, a large number of conferences have had to face hard choices, several which decided going fully virtual. This hard choice has now open the doors to people from around the world to gain access to the great online content generated by expert speakers and contributors. We wanted to highlight some of these key conferences so they are not missed - these include:
Did we miss any? Please let us know by replying to the newsletter email or by simply emailing us at
The topic for this week's featured production machine learning libraries is Privacy Preserving ML. 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:
  • Substra - Substra is an open-source framework for privacy-preserving, traceable and collaborative Machine Learning.
  • Tensorflow Privacy - A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.
  • TF Encrypted - A Framework for Confidential Machine Learning on Encrypted Data in TensorFlow.
  • Uber SQL Differencial Privacy - Uber's open source framework that enforces differential privacy for general-purpose SQL queries.
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
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:
  • 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
© 2018 The Institute for Ethical AI & Machine Learning