The Institute for Ethical AI & Machine Learning

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THE ML ENGINEER 🤖 Issue #142     This week in Issue #142:   Principles for Digital Citizenship Stories from ML Practitioners Drift Without Labelled Data Software Design for Data Scientists Data Quality Unpacked 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!       Principles for Digital Citizenship Our submission for the European Commission consultation on the Foundational Principles for Digital Citizenship has now been published 🚀 This contribution includes comments that emphasise the importance of universal access to internet & services, ethical principles for human-centric algorithms and a broad range of other critical topics.       Stories from ML Practitioners ApplyingML has put together a set of compiled interviews with seasoned practitioners in the machine learning space, including senior, staff and director level experiences across tech companies like Amazon, Databricks, Spotify and more.       Drift Without Labelled Data A comprehensive article that provides a deep dive into concept drift techniques, as well as best practices on how to combat the divergence between static models and dynamic environments.       Software Design & Data Scientists A great resource that provides best practices around software design for data scientists, and outlines the motivations, principles and hands on examples for the various concepts it introduces.       Data Quality Unpacked The rise of DataOps has emphasised the importance of data quality as opposed to just quantity. This article provides an overview of some core concepts of data quality, but also the reasoning for the growing ecosystem of solutions to address these challenges.       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