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THE ML ENGINEER 🤖
Issue #91
 
 
This week in Issue #91:
 
 
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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 a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
 
 
 
Machine learning fits incredibly well to the parallel-processing architecture that GPU computing offers. Projects like the Vulkan OSS initiative have enabled for cross-platform, cross-vendor, mobile-enabled advanced data-processing GPU applications. This article provides an intuitive hands on tutorial that teaches you to build your own machine learning algorithm from scratch in only a handful lines of code using the Vulkan Kompute framework.
 
 
 
Join us this coming week at our online AI, Data & Ethics event, where Prof. Joanna Bryson will share her insights on AI, Data & Ethics in 2020, as well as on her research around accountability for and transparency in AI, technological impact on human cooperation, and beyond.
 
 
 
The Data Exchange podcast comes back this week with a fascinating conversation with Weifeng Zhong, Senior Research Fellow at George mason University. He is the core maintainer of the OSS Policy Change Index, a framework that uses ML and NLP to process government priorities and policies.
 
 
 
LLVM co-creator and Swift Language designer, Chris Lattner, joins TheNewStack editor David Cassel on a very interesting conversation around his current/future work, together with his thoughts on the future of programming itself. This article covers some key highlights, including some learnings and retrospective thoughts on Swift, as well as his thoughts on machine learning in code compilers. The full video is also available in the article.
 
 
 
O'Reilly's Mike Loukides has put together an overview of an AI O'Reilly Radar on trends to watch in 2020. This covers some key highlights across AI, General Programming, Cloud & Microservices, innovations in infrastructure and beyond.
 
 
 
 
 
 
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 UK-based research centre that carries out world-class research into responsible machine learning.
 
 
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