The MachineLearning EngineerIssue #111

This week in Issue #111:

• Top 2020 ML Notebooks
• Massive Scale Kubernetes for AI
• Tech in 2020 Data Science
• Architecture of ML Systems
• State of AI Ethics Report 2021
• 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!

Top 2020 ML Notebooks

An interesting analysis of key stats and trends of jupyter notebooks on Github, together with statistics on the popularity of python libraries for ML used, and relevant linked trends from youtube/google/data sources.

Massive Scale Kubernetes for AI

OpenAI shares insights on how they’ve pushed their Kubernetes cluster towards massive scale for their large models like GPT-3, CLIP and DALL-E, but also for rapid smaller-scale iterative research initiatives such as scaling laws for neural language models.

Tech in 2020 Data Science

An analysis of 30,000 unique data science blog posts from 2020 to identify the most discussed topics and technologies. This blog post provides deeper insights on software tools, programming languages, cloud platforms and data science platforms.

Architecture of ML Systems

This short video series aims to introduce core concepts in software architecture and software requirements planning into production machine learning systems. It covers the motivations, approaches towards requirements gathering, methodologies to take technical decisions, software architecture diagram approaches, communication, documentation, skills and personal development

State of AI Ethics Report 2021

The Montreal AI Ethics Institute has published “The State of AI Ethics Report (January 2021)” which captures the most relevant developments in AI Ethics since October of 2020. In this report they cover 8 key themes including: 1) Algorithmic Injustice, 2) Discrimination, 3) Ethical AI, 4) Labor Impacts, 5) Misinformation, 6) Privacy, 7) Risk & Security, and 8) Social Media.

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 Vulkan compute framework optimized for advanced GPU 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!