The MachineLearning EngineerIssue #100

Today we celebrate together our 100th Issue 🚀🚀🚀 This is a big day as we are able to also celebrate several achievements:

• MLE Newsletter reached 4500 subcribers 🥳
• Production ML List reached 7000 Stars 🎈
• XAI Toolset reached 500 Stars 🎉
• Kompute Framework reached 180 Stars 🎁

We want to thank YOU for supporting this newsletter and our work, we look forward to continue driving forward and contributing to the conversations of machine learning engineering 🚀 bring on 100 more!

This week in Issue #100:

• Beyond CUDA: Cross-vendor GPU Python ML
• ML Metadata Management Tools
• Practical Guide To Responsible AI
• Navigating ML Deployment
• Stanford MLSys Seminars
• Featured OSS Production ML Libraries
• Awesome AI Guidelines to check out this week
• + more 🚀

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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!

Cross-vendor GPU Python ML

Machine learning algorithms — together with many other advanced data processing paradigms — fit incredibly well to the parallel-architecture that GPU computing offers. In this article you’ll learn how to write your own GPU accelerated algorithms in Python, which you will be able to run on virtually any GPU hardware, including non-NVIDIA GPUs.

ML Metadata Management Tools

Machine learning metadata management is becoming a key challenge in production AI systems. We have started mapping the ecosystem of Open Source metadata management frameworks - check out the tools and like always contribution are greatly appreciated.

Practical Guide To Responsible AI

Our talk at the PyData Global has now been published, where we cover the principles, standards, tools and practical frameworks towards responsible development and operation of AI systems.

Navigating ML Deployment

Seldon MLOps Developer Ryan Dawson has put together a fantastic article covering the concepts, challenges and solutions around machine learning model deployment and orchestration.

Stanford MLSys Seminars

Stanford has published their seminar videos on the frontier of machine learning systems, covering key concepts around challenges and solutions in AI research and industry in conversation with thought leaders in the space.

OSS: Metadata Management

The topic for this week’s featured production machine learning libraries is Metadata Management. 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:

• Amundsen - Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.
• Apache Atlas - Apache Atlas framework is an extensible set of core foundational governance services – enabling enterprises to effectively and efficiently meet their compliance requirements within Hadoop and allows integration with the whole enterprise data ecosystem.
• DataHub - DataHub is LinkedIn’s generalized metadata search & discovery tool.
• Metacat - Metacat is a unified metadata exploration API service. Metacat focusses on solving these three problems: 1) Federate views of metadata systems. 2) Allow arbitrary metadata storage about data sets. 3) Metadata discovery.

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!