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Issue #167 THE ML ENGINEER 🤖   This #167 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 #167:   ML Monitoring with Outliers & Drift Netflix Infra Systems Design Building Infrastructure Platforms Uber Orbit Bayesian ML Platform The State of ML in JuliaLang 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!       ML Monitoring with Outliers & Drift The lifecycle of a machine learning model only begins once it’s in production. This week Seldon Engineering Director & IEML Chief Scientist Alejandro Saucedo will be giving a live webinar on production machine learning monitoring covering how to introduce operational and data science monitoring (outliers & drift) at scale.     Netflix Infra Systems Design Systems Design skills are key for ML & MLOps engineers; in this article Amazon Senior ML Engineer covers in detail a systems design interview type question covering an approach to explore the question of designing the infrastructure behind Netflix from scratch, covering the requirements gathering, design, technical considerations, APIs, etc.     Building Infrastructure Platforms Thoughtworks Engineering has put together a comprehensive overview that explores the challenge of developing infrastructure platforms through 7 key principles that identify key important areas that allow for teams to focus on building on functionality that will bring value to the ultimate users.     Uber Orbit Bayesian ML Platform Uber Engineering has put together an overview of Orbit, their Bayesian time-series analysis and forecasting library. In this article they cover some of the key features as well as provide insights on how Uber built an internal platform to productise their user-base workflows across their organisation.     The State of ML in JuliaLang A very interesting discussion in the Julia community forum providing insights on the state of machine learning in the Julia language, diving into various key questions that highlight some of the key achievements, opportunities and gaps.     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