The Institute for Ethical AI & Machine Learning

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THE ML ENGINEER 🤖 Issue #148     This week in Issue #148:   Linkedin's Approach to XAI Prod ML Resources Curated List Top Places to Work for ML ML, AI & Data Landscape (MAD) Training System for Industry Scale 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!       Linkedin's Approach to XAI An overview from Linkedin Engineering on their Responsible AI program. They dive into high level principles, architectural blueprints, techniques used, processes, and brief insights on their future work.       Prod ML Resources Curated List A comprehensive github list containing an extensive set of curated papers, articles and blogs on data science and machine learning in production, ranging across a broad range of different areas.       Top Places to Work for ML A great resource that explores top organisations for data scientists of different levels of seniority, covering also the methodology they used to approach this question.       ML, AI & Data Landscape (MAD) This latest landscape maps an extensive list of technologies across a broad set of different areas and themes involving the full end to end machine learning lifecycle, focusing primarily on "MAD" suppliers.       Training System for Industry Scale The Facebook team has put together a great overview of a large effort to identify scalable approaches and systems to achieve the data ingestion for large-scale training of recommender system models.       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:   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!     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.   Check out our website   © 2018 The Institute for Ethical AI & Machine Learning