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Issue #162 THE ML ENGINEER 🤖    This #162 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 #162:   MLOps Meetup Online this Week Architecture Series for MLOps Testing Approach in Data Science A Gentle Intro to Shapley Values Kubernetes the Documentary 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!       MLOps Meetup Online this Week The MLOps London meetup comes back this week with a fully online edition bringing together insights from FuzzyLabs Cofounder Matt Squire and Trainline Principal Data Scientist Jan Teichmann to share knowledge on various practical production MLOps topics.     Architecture Series for MLOps An interesting practical tutorial series that provides hands on insights on production architectures for MLOps, specifically focusing on the feature extraction, process management, serving infrastructure and monitoring phases of the ML lifecycle with a practical use-case.     Testing Approach in Data Science A hands on article convering an interesting perspective into the software engineering best practices of unit testing applied into data science, covering a set of practical examples.     A Gentle Intro to Shapley Values The H2O team has put together a comprehensive overview into one of the more popular methods in the machine learning explainability space, namely shapley values, together with a set of examples into how these values can be calculated for a feature.     Kubernetes the Documentary A recently released documentary which covers the history of Kubernetes and container applications - this is the first part of an upcoming series of episodes.     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!        © 2018 The Institute for Ethical AI & Machine Learning