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Issue #87
We've hit a significant milestone this week 🚀 Our Production ML Repository has reached over 5700 stars, with exactly 100 closed PRs from over 60 contributors 🔥🔥🔥 We have also reached over 3500 subscribers! Massive thank you to all our community members and Institute Fellows for all your support ✨👏🎉😃 #LetsKeepDoingThis
This week in Issue #87:
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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!
Anyscale's Advisor Ben Lorica and Chairman Ion Stoica have put together a comprehensive overview of the state of machine learning model servers in production, as well as the core components that constitute full-featured systems. In this article they delve into toolkit support, user interfaces, ability to scale, deployment strategies, monitoring and more.
Causal effects methods can provide rich information for decision making, such as in experimentation platforms (“XP”) or in algorithmic policy engines. Every Netflix data scientist, whether their background is from biology, psychology, physics, economics, math, statistics, or biostatistics, has made meaningful contributions to the way Netflix analyzes causal effects - this article provides a high level insight to some of these.
The Practical AI podcast has reached their 100th episode. In this edition the Practical AI team has released the podcast together with a giveaway for prizes. In this edition they delve into conversation with the Pachyderm team, together with some big announcements from their data science and machine learning provenance platform.
Stanford University researchers have published a fascinating paper together with a showcase of their algorithm simulating a realistic video of a tennis game. In their work they are able to convert annotated broadcast videos of tennis matches into interactively controllable video sprites that behave and appear like professional tennis players. This is quite an impressive demonstration which provides an intuition of the potential AI technology can have.
The Jupyter Book has been re-written from the ground up, making it easier to install, faster to use, and able to create more complex publishing content in everyone's books. The new jupyter book comes with a broad set of new features that will help both researchers and practitioners.
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
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 Europe-based research centre that carries out world-class research into responsible machine learning.
© 2018 The Institute for Ethical AI & Machine Learning