The Institute for Ethical AI & Machine Learning

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THE ML ENGINEER 🤖 Issue #128     This week in Issue #128:   Key AI Research Labs In Europe Responsible AI at Linkedin Lessons from Netflix, Spotify, etc Twitter on Elastic + Neural Nets Three ML Roles in Organisations Open Source ML Frameworks Awesome AI Guidelines to check out this week + more 🚀   Forward  email, or share the online version on 🐦 Twitter,  💼 Linkedin and  📕 Facebook!   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!       Key AI Research Labs In Europe The Open Data Science Conference (ODSC) team have put together a great overview of "Europe Research Labs to Watch", and we are thrilled that they have featured The Institute for Ethical AI & ML, together with other great academic and industry research labs.       Responsible AI at Linkedin An interesting introspective post from Linkedin analysing and reviewing what Responsible AI means internally. In this post they cover the areas of Fairness and Privacy as well as the road ahead.       Lessons from Netflix, Spotify, etc. This article analyses the end to end lifecycle of ML and Data, as well as the approach & tools used by leading tech organisations including Netflix, Intuit, Intel, etc. covering the areas of model registries, feature stores, workflow orchestration, serving and monitoring, also showcasing that most of the tools used include a combination of in-house systems with open source frameworks.       Twitter on Elastic + Neural Nets A fascinating post from Twitter Engineering around how they developed a recommendation engine to annotate legacy datasets to a new standardized taxonomy. This is very relevant as the importance of metadata grows across organisations, which often comes with manual labor required to annotate the required metadata retrospectively.       Three ML Roles in Organisations An interesting conceptual review of the roles and their responsibilities in organisations building ML & Data systems. In this post it is proposed that the three key roles involve the Data Warehouse Engineers, Data Science Analysis and Data Scientists (which encompasses DS and MLOps / DevOps).     OSS: GPU Accel. Frameworks   The topic for this week's featured production machine learning libraries is GPU Acceleration 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:   Vulkan Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced  data processing usecases. CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.   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!        OSS: Awesome AI Guidelines   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:   AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles 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