The Institute for Ethical AI & Machine Learning

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THE ML ENGINEER 🤖 Issue #130     This week in Issue #130:   All-Things-Python at Netflix Monitoring ML Systems Course AI Risk and Liability in Industry Kubernetes Learning from Scratch What is Your ML Model Hiding Open Source ML Frameworks Awesome AI Guidelines to check out this week + more 🚀   Forward  email, or share the online version on 🐦 Twitter,  💼 Linkedin and  📕 Facebook!   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!       All-Things-Python at Netflix A great high level overview of how Python has been adopted and is being used at Netflix. The use of Python ranges across various systems and teams, including their CDN services, demand engineering teams, core engineering teams, monitoring (alerting & remediation), information security, machine learning, ML infrastructure, experimentation notebooks and more.       Monitoring ML Systems Course A fantastic resource that covers real life and cutting edge techniques for advanced machine learning monitoring. In this hands on course, they cover the theory and practical examples on systems monitoring, drift, outliers, alerting, and more.       AI Risk and Liability in Industry The data exchange podcast dives into conversation with BHN.ai Co-founder Andrew Burt. In this podcast they dive into a broad range of topics including Andrew's work at BHN.ai which focuses on legal services around AI compliance and risk mitigation. They also cover practical insights on how companies are tackling these issues in critical and production environments.       Kubernetes Learning from Scratch Kubernetes is becoming ubiquitous in production systems - this resource provides a comprehensive learning path for interested practitioners that want to learn how to develop and operate systems in kubernetes clusters. It covers the basics for application deployment and then dives into the internals for operations, as well as for extension of the Kubernetes APIs.       What is Your ML Model Hiding This blog post covers the motivations, concepts and techniques that enable machine learning practitioners to interpret and evaluate models. They cover various techniques and best practices that can be adopted in the data science lifecycle.       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:   Vulkan 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