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Issue #69
This week in Issue #69:
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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!
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
CometML has put together a great article that outlines best practices for managing remote data science teams. The article includes key considerations, including ortanisational structures, biggest challenges that remote workers face, and best practices; these include productive workspaces, communication, habits, trust and more.
A great Data Exchange Podcast with Machine Learning Consulting CEO Rob Munro, where they dive into "Human in the loop Machine Learning", and cover Rob's experience at various tech giants, writing his book on the topic, several NLP areas where it's relevant, and how this fits in real life.
Netflix brings us a high level overview of how they use Druid for real time insights. Apache Druid is a high performance real-time analytics database, which is designed primarily for workflows where fast queries and ingest really matter. In this post they highlight how Druid's capabilities shine around instant data visibility, ad-hoc queries, operational analytics and handling high concurrency. They cover a high level architecture of their data processing lifecycle, as well as insights they have gathered to ensure scale.
The O'Reilly team published a great post that highlights the importance of data preparation. In this article they present a "Data Science Hierarchy of Needs", where they outline how key data processing is to ensure accurate and reliable insights when tackling any data-related challenge. In the post they cover the importance of automatic data prep, some key tools aiding in this area, and they dive into the future of tooling.
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:
  • Redash - Redash is anopen source visualisation framework that is built to allow easy access to big datasets leveraging multiple backends.
  • Plotly Dash - Dash is a Python framework for building analytical web applications without the need to write javascript.
  • Streamlit - Streamlit lets you create apps for your machine learning projects with deceptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file
  • PDPBox - This repository is inspired by ICEbox. The goal is to visualize the impact of certain features towards model prediction for any supervised learning algorithm. (now support all scikit-learn algorithms)
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.
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© 2018 The Institute for Ethical AI & Machine Learning