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Issue #163 THE ML ENGINEER 🤖    This #163 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 #163:   CI/CD for Prod ML at Scale Event Machine Learning at Discord Kubernetes the Documentary Pt.2 Open Source MLOps E2e Stacks ML Algorithms Cheat Sheet 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!       CI/CD for Prod ML at Scale Event IEML Chief Scientist & Seldon Engineering Director Alejandro Saucedo is covering best practices, architectural patterns and practical examples of continuous integration/delivery for production machine larning at massive scale, enabling for thousands of models in Kubernetes.     Machine Learning at Discord The Data Exchange podcast comes back this week to dive into conversation with Discord Senior Machine Learning Engineering Manager Gaurav Chakravorty to share insights on building industrial grade machine learning systems for search, recommenders, and personalization systems at Discord.     Kubernetes the Documentary Pt.2 The second part of the positively received Kubernetes documentary is out. A fantastic overview of the history and principles behind this successful open source orchestration framework which is increasingly adopted in the MLOps space, diving into fascinating insights on relationships and tensions with Mesos, Docker, CentOS and beyond.     Open Source MLOps E2e Stacks Fuzzylabs Cofounder Matt Squire joined the MLOps London Meetup last week to cover their version of their canonical stack for MLOps, covering key OSS projects including DVC, Sacred, ZenML, Seldon Core and Evidently.     ML Algorithms Cheat Sheet A great resource that aims to provide a high level cheatsheet for a broad range of machine learning algorithms, together with a visual grouping that allows for fast lookup for refrence.     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