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Issue #102
This week in Issue #102:
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Microsoft Principal Software Engineer Saquib Shakh is leading a fantastic initiative to increase the number of AI use-cases currently in place to empower individuals with impaired vision. This is part of a broader initiative to create further AI use-cases that can impact people's lives positively by making usability key component in developing any type of AI applications.
Scale has played a central role in the rapid progress natural language processing has enjoyed in recent years. While benchmarks are dominated by ever larger models, efficient hardware use is critical for their widespread adoption and further progress in the field. In tutorial, Google Engineers and University of Washington Researchers cover a wide range of techniques to improve efficiency in NLP models.
Following the announcements of Airbnb, Lyft, Netflix and Uber on their custom-built internal metadata management and discovery platforms, Facebook presents their approach to metadata discovery infrastructure. In this article they cover a high level overview of their "Nemo" metadata system, including the architecture, and brief lessons learned.
Netflix showcases their real time processing framework "Bulldozer". In this article they present how they are experimenting as they explore introducing more interoperability between offline batch use-cases and online key value store-based processing architectures.
"ML Street Talk" has published a great content-dense podcast. In this edition they speak with Professor Gary Marcus, Dr. Walid Saba and Connor Leahy about GPT-3. They also invite the audience into their in-depth experimentation with the GPT-3 APIs, where they delve into demos and use-cases.
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