The MachineLearning EngineerIssue #123

This week in Issue #123:

• CERN on Scaling 600 Clusters
• Real time Data Infra at Uber
• OpenAI Powered Linux Shell
• DevSecOps for Machine Learning
• Python 3.10 Feature Highlights
• 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!

Real time Data Infra at Uber

Uber Engineers Yupeng Fu and Chinmay Soman share key insights on Uber’s data infrastructure in a recent whitepaper - they cover the overall architecture of the real time infrastructure and identify scaling challenges that they need to continuously address for each component in the architecture.

CERN on Scaling 600 Clusters

CNCF’s VP Ecosystem Cheryl Hung interviews CERN computing engineer Ricardo Rocha and delves into the scale of CERN’s infrastructure managing 600 clusters.

OpenAI Powered Linux Shell

A fascinating application of OpenAPI’s GPT3 API showcasing how to leverage these models specifically for automatic text to bash-scripts applications. This provides an intuition of the potential of these emerging technologies, but also the importance of human in the loop, in this case to avoid an accidental “sudo rm -rf /”.

DevSecOps for Machine Learning

Security in the MLOps lifecycle is key - Ian Hellstrom shares in a latest blog post some key principles for Development Security Operations for Machine Learning, or DevSecOps for ML. This article dives into vulnerability scalling and benefits of distroless images.

Python 3.10 Feature Highlights

A great overview of key features coming in with Python 3.10, including type checking improvements, type aliases syntax, population count, context manager syntax, and more.

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!