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Issue #57
This week in Issue #57:
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Google reaserch has put together a very comprehensible overview of their key highlights and results from 2019 as well as their focus for 2020 and beyond. In this post they touch upon ethical use of AI, AI for social good, Applications of AI in Other Fields, Assistive Technology, Use of AI in Mobile Devices, Quantum Computing, AutoML and more.
Facebook dives into their open source year in review list, where they cover high level overview of their open source projects and the achievements/updates in 2019, including their ~2500 contributors and 32000 contributions. In this article they cover their open source frameworks PyTorch, Hydra, Calibra as well as other Open Source partnerships.
Chief Scientist Ben Lorica comes back with another Data Exchange podcast. This time he dives into lessons learned with Rakuten Data Science VP Bahman Bahmani. In this podcast they cover
the impact that machine learning in Rakuten, best practices in attracting/retaining ML talent, the trio of strategic options and culture within the organisation.
The AI Governance Dilemma: As machine learning is adopted in more critical use-cases, the common phrase that tech startups have used "move fast and break things" becomes less desired. In this great article by Seldon Open Source Engineer Ryan Dawston, this AI Governance Dilemma is broken down. Ryan provides an introduction to the challenges of ML being deployed in critical use-cases, together with the different areas that should be taken into account, including outliers, concept drift, bias, privacy and other risks.
Intro to Ethics in AI: The discussion of ethics in AI has become more critical as more applications make their way into production environments that affect the real world. We're organising a London meetup on January 24th covering an introduction to AI, where HATLAB Deputy Director James Kingston will help us get our bearings by taking us on a survey of an AI Ethics Landscape, followed by an open discussion. Come join us!
OSS: Feature Engineering
We're excited to add a new section into our Production ML Libraries which focuses on Feature Stores. 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:
  • Hopsworks Feature Store - Offline/Online Feature Store for Machine Learning.
  • Feature Store for Machine Learning (FEAST) - Feast (Feature Store) is a tool for managing and serving machine learning features. Feast is the bridge between models and data.
  • Veri - Veri is a Feature Label Store. Feature Label store allows storing features as keys and labels as values. Querying values is only possible with knn using features. Veri also supports creating sub sample spaces of data by default.
  • Ivory - ivory defines a specification for how to store feature data and provides a set of tools for querying it. It does not provide any tooling for producing feature data in the first place. All ivory commands run as MapReduce jobs so it assumed that feature data is maintained on HDFS.
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 showcasing three resources from our list so we can check them out every week. This week's resources are:
If you know of any guidelines that are not in the "Awesome MLOps" list, please do give us a heads up or feel free to add a pull request
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