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Issue #161 THE ML ENGINEER 🤖    This #161 edition of the ML Engineer newsletter contains curated articles, tutorials and blog posts from experienced Machine Learning and MLOps professionals. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions 🚀   If you like the content please support the newsletter by sharing on 🐦 Twitter,  💼 Linkedin and  📕 Facebook!         This week in Issue #161:   Principles for Responsible AI Google Research ML Themes Introduction to Explainable ML ML Architectures from Industry Validation & Testing of ML Models Open Source ML Frameworks Awesome AI Guidelines to check out this week + more 🚀   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!       Principles for Responsible AI Our VentureBeat article on Responsible AI is now live 🚀 AI will become ubiquitous over the next decade and like any technology, it poses personal, societal, and economic risks. In this article we dive into some of the principles from The Institute for Ethical AI & Machine Learning that ensure the responsible development, design and operation of AI systems.     Google Research ML Themes The Google Research team has published an overview of the key research themes at Google from 2021 and beyond, covering general-purpose ML models, efficiency improvements, growing benefits, and deeper understanding.     Introduction to Explainable ML The team behind the Alibi Explain project has put together a comprehensive introducion and deep dive into the key concepts of explainability in machine learning, including intuition, background, examples and relevant references.     ML Architectures from Industry An interesting exploration across the growing number of architectures from leading tech giants on production machine learning systems, diving into common patterns, components across the ML lifecycle, requirements for massive scale and more.     Validation & Testing of ML Models As part of the continued trend of software engienering best practices making it to the machine learning lifecycle, this interesting article provides a practical overview on how to introduce robust validation and testing to the machine learning lifecycle leveraging the Deepchecks tooling.     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!       © 2018 The Institute for Ethical AI & Machine Learning