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Issue #101
We are still buzzing from last week's celebration of our 100th Issue 🚀🚀🚀 We will want to continue the celebration for yet another week on the accomplishments so far and of course to extend our gratitude to all our community and newsletter subscribers! That is, celebrating the following milestones:
Once again we want to thank YOU for supporting this newsletter and our work, we look forward to continue driving forward and contributing to the conversations of machine learning engineering 🚀
This week in Issue #101:
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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!
The lifecycle of a machine learning model only begins once it's in production. Our keynote at Pycon HK 2020 is now available online, where we delve into the end to end principles, patterns and techniques around production machine learning monitoring. We cover AI monitoring through explainability, outlier detection, concept drift and statistical performance from a theoretical and practical hands-on standpoint.
TectonAI has published a high level overview that breaks down the concepts and components of feature stores from an architectural standpoint. They mention the nuances of serving, storage, data transformations, monitoring and registries.
The research area around automated optimizations on low level intermediate code representation is incredibly fascinating. This talk provides an introduction and an overview of how this is being achieved through advanced reinforcement learning techniques.
Explainability techniques in machine learning have seen applicability in a broad range of highly regulated sectors. ETH Zurich researchers have published an interesting paper in Nature covering how these techniques can be used in the drug discovery space. In this resource they  provide an overview as well as a deep dive around the tools available in the ecosystem.
Cambridge researchers have published a survey that explores the challenges of deploying machine learning in production. In this paper they delve into findings gathered from a broad range of practitioners deploying machine learning in industry, and cover the nuanced issues across the various phases of the end to end machine learning lifecycle.
The topic for this week's featured production machine learning libraries is Metadata Management. 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:
  • Amundsen - Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.
  • Apache Atlas - Apache Atlas framework is an extensible set of core foundational governance services – enabling enterprises to effectively and efficiently meet their compliance requirements within Hadoop and allows integration with the whole enterprise data ecosystem.
  • DataHub - DataHub is LinkedIn's generalized metadata search & discovery tool.
  • Metacat - Metacat is a unified metadata exploration API service. Metacat focusses on solving these three problems: 1) Federate views of metadata systems. 2) Allow arbitrary metadata storage about data sets. 3) Metadata discovery.
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 these 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. You can find multiple principles in the repo - some examples include the following:
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