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Issue #85
This week in Issue #85:
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The team at MentorCruise asked dozens of experts and professionals in Machine Learning about their favourite books, and they have compiled a list of their top recommendations. In this list they further categorised the books by topic, including fundamentals, coding, practical ML, specializations, and theory.
Over 7000 languages are spoken around the world, but NLP research has mostly focused on English. Sebastian Ruder has put together an article that outlines why you should work on languages other than English, including motivations, potential and action points.
The Kubeflow team has launched version 1.1 of their end-to-end cloud native machine learning ecosystem. They have put together a post that outlines the key focuses, which improve the ML Workflow Productivity, Isolation, Security, GitOps and beyond.
Machine Learning Mastery has put together a hands on tutorial that showcases how to leverage k-fold cross validation, which is a foundational and important concept in machine learning model evaluation. In this tutorial they cover how to evaluate ML models, how to perform sensitivity analysis for k-fold cross validation, and how to calculate correlations between cross-validation tests and ideal test conditions.
Nine philosophers explore the various issues and questions raised by the newly released language model, GPT-3. The content contributed from these philosophers is broken down into further 9 topics, including consciousness, justice and creativity.
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
© 2018 The Institute for Ethical AI & Machine Learning