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Issue #166 THE ML ENGINEER 🤖   This #166 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 #166:   Data Scientists and Kubernetes Cross Vendor GPU Acceleration Open Python ML Inference Server Building ML Infra at Netflix Interpretable Machine Learning 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!       Data Scientists and Kubernetes The relatively new startup behind the Metaflow Open Source project has put together a fantastic introduction as well as hands on examples that reflect the principle "Data Scientists Don’t Need to Know Kubernetes with Metaflow".     Cross Vendor GPU Acceleration Our CppCon2021 talk is out and has been gathering momentum in the C++ community, covering best practices for GPU acceleration across cross-vendor GPUs using Vulkan and the Kompute project, as well as hands on examples for ML and low-level optimizations.     Open Python ML Inference Server The team at Seldon has announced the 1.0 release of an ML inference server that focuses on Python based models, providing extendable runtimes to create reusable runtimes, and key features such as MLFlow integration, multiprocessing, adaptive batching + more.     Building ML Infra at Netflix The Data Exchange podcast dives into conversation with Outerbounds Co-founder Savin Goyal to discuss about his previous work at Netflix led into the open sourcing of Metaflow, a framework to adddress challenges of data science around version control and scalability.     Interpretable Machine Learning A fantsatic resource on interpretable machine learning which dives into a robust taxonomy for explainability related terms, as well as a deep dive into the current state of the ecosystem.     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