The Institute for Ethical AI & Machine Learning

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THE ML ENGINEER 🤖 Issue #119     This week in Issue #119:   Andrew Ng deep dive on MLOps ML & DL Online Courses Playbook for Model Monitoring Continuous Retraining Strategy Building a Database from Scratch Featured OSS Production ML Libraries 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!       Andrew Ng deep dive on MLOps A great overview of MLOps by Andrew Ng. This talk provides a foundational intuition coming from the core machine learning principles, and introduces the challenges around deployed models such as re-training, bias/variance, and general DevOps / ML Engineering challenges.       ML & DL Online Courses Facebook AI PM Elivs has put together a list of machine learning and deep learning courses which contain extensive content spanning across a broad range of ML areas.       Playbook for Model Monitoring Arize AI Co-founder Aparna Dhinakaran has put together a playbook that outlines the challenges in prod ML connected to availability of ground truth with deployed models, as well as the metrics available that can be used for each scenario.       Building a Database from Scratch A fascinating resource that provides a hands on intuition of the internals of databases by guiding us through the development of a simple database from scratch using the C language. It covers foundational concepts such as data saving formats, moving memory, primary keys, transaction rollbacks and more.       Continuous Retraining Strategy Data Scientist Or Itzary shares a conceptual framework that enable practitioners to assess when models should be retrained, as well as the data that should be used in the context of production deployments.       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