The MachineLearning EngineerIssue #137

This week in Issue #137:

• Uber Distributed Computing AI
• Building Robust ML Workflows
• Bjarne on Future of Programming
• Papers with Code Highlights
• A Base ML Project Starter
• Open Source ML Frameworks
• 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!

Uber Distributed Computing AI

Uber engineering shares in this blog post a framework called Fiber which enables for simple distributed processing, specifically focused on AI frameworks, covering some specialised algorithm implementations.

Building Robust ML Workflows

A collaboration between Pachyderm and Seldon showcasing how to build robust ML workflows that can cover the end to end ML lifecycle spectrum.

Bjarne on Future of Programming

C++ Creator Bjarne Stroustrup dives into a conversation where he shares his thoughts on the future of programming, including the impact of AI in programming language particularly in the context of compiler optimization.

Papers with Code Highlights

Papers with Code releases a new feature to allow researchers to keep track of trending papers discussed by the community through “hot” category sorting.

A Base ML Project Starter

A great set of resources for kickstarting ML projects, including a foundation codebase to accelerate getting started, as well as an overview of the key concepts to take into consieration, followed by 5 more extension blog posts.

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!