The MachineLearning EngineerIssue #141

This week in Issue #141:

• Kompute Joins the Linux Foundation
• MLOps London Meetup Kickoff
• Feature Stores Demystified
• Data Science Efficient Python
• Auditing ML for Compliance Risk
• 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!

Kompute Joins Linux Foundation

The Kompute Project joins the Linux foundation to further the cross-vendor GPU Computing ecosystem for AI, Machine Learning and Advanced Data Processing use-cases, enabling practitioners to accelerate their applications across AMD, NVIDIA, Qualcomm & Friends over 1000s of graphics cards 🚀

MLOps London Meetup Kickoff

MLOps London is a new meetup that is looking to bring together practitioners that work with production machine learning, and will be doing their first kick-off event this coming month which can be joined both online and in-person.

Feature Stores Demystified

The concept of feature stores has been growing in popularity in recent years, and the concepts it encompasses tend to be relatively ambiguous. This post provides a comprehensive overview of the concept, motivations, architectures and use-cases for feature stores.

Data Science Efficient Python

A great short article that provides practical tips and tricks when using python for data science through useful libraries, frameworks and code snippets.

Auditing ML for Compliance Risk

Distinguished CMU ML Professor Rayid Ghani dives into conversation together with BNH.ai cofounder Andrew Burt in the Data Exchange podcast where they discuss how organisations are developing ways to audit ML models for discrimination, bias and other risks.

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!