Subscribe to the Machine Learning Engineer Newsletter

Receive curated articles, tutorials and blog posts from experienced Machine Learning professionals.

Issue #73
This week in Issue #73:
Forward  email, or share the online version on 🐦 Twitter,  💼 Linkedin and  📕 Facebook!
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!
This weekend we have a talk at PyConHK 2020 about real time machine learning using Python, Faust, Kafka and Seldon. During this talk we dive into the core concepts of stream processing, as well as the ecosystem of tools available and a hands on example building a streaming pipeline with multiple workers processing real time comments from Reddit's /r/science subreddit using 200k comments the "comments removed by mods" dataset.
We have been contributing to a position statement on Contact Tracing applications through our role at the Association for Computer Machinery (ACM)'s European Policy Committee. This statement provides a set of principles and recommendations to countries that are looking to use contact tracing to tackle the challenges COVID has been posing in our societies. This Friday we're also organising a virtual meetup where the Hatlab Deputy Director will be sharing insights on contact tracing apps.
The Eighth Conference on Learning Representations took place virtually in 2020, and has recently released the videos for the all the talks, which are now available at their website. This is a fantastic resource that provides access to state of the art research in a digestible format, and provides for an open resource from which other researchers and practitioners will be able to build upon.
The Data Exchange Podcast comes back this week with a fantastic session with Staff Research Engineer Pete Warden. This episode covers the early days of deep learning for computer vision, core early days of the tensorflow project, insights on TinyML and why it's such an important topic, privacy in the context of tinyML, and Pete's new book.
The PapersWithCode project has released a new update, where they share some insights on the challenge of reproducibility that they have been tackling with this project. They are introducing new exciting features, including new results interface, an ML Extraction Algorithm that automatically extracts results from papers, and a Big Database update with 800+ new leaderboards, 550+ new results and more.
[Updated List 10/05/2020] 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 Data Visualisation. 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:
  • XAI - eXplainableAI - An eXplainability toolbox for machine learning.
  • Microsoft InterpretML - InterpretML is an open-source package for training interpretable models and explaining blackbox systems.
  • SHAP - SHapley Additive exPlanations is a unified approach to explain the output of any machine learning model.
  • ELI5 - "Explain Like I'm 5" is a Python package which helps to debug machine learning classifiers and explain their predictions.
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
About us
The Institute for Ethical AI & Machine Learning is a UK-based research centre that carries out world-class research into responsible machine learning systems.
Check out our website
© 2018 The Institute for Ethical AI & Machine Learning