The MachineLearning EngineerIssue #149

This week in Issue #149:

• NeurIPS Practical Responsible AI
• Github on Deploying ML Safely
• The State of AI Report 2021
• Shopify on Building ML Models
• 20 Lessons Across 20 Years
• 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!

NeurIPS Practical Responsible AI

We are thrilled to announce that Chief Scientist Alejandro Saucedo will be giving a keynote at NeurIPS 2021 at the LXAI workshop this coming December 7th 🚀 The virtual tickets are free so come join us at this fantastic event to hear about a broad range of topics in state of the art machine learning research & applications. Until then, you can also join upcoming talks this week at CppCon 2021 and PyData Global 2021 🤖

Github on Deploying ML Safely

Github Staff Machine Learning Engineer Hamel Husain joins the Data Exchange podcast and provides great insights on the areas of CI/CD for ML, MLOps tools and processes, and how much software engineering should data scientists know.

The State of AI Report 2021

The Annual State of AI report has come out, providing an outline of very interesting development of AI, and covers the areas of research, talent, industry, politics and predictions.

Shopify on Building ML Models

A great practical perspective from Shopify Engineering on their perspective around building machine learning models in industry, which covers practical advise to question the relevance, requirements, measurements, plans, and stability when considering ML in practical contexts.

20 Lessons Across 20 Years

A great high level article providing 20 points of advise from a long career in software engineer, aiming to outline a set of principles that are important to consider for a meaningful and consciencious approach to software as a applicable and useful craft.

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:

• 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!

About us

The Institute for Ethical AI & Machine Learning is a UK-based research centre that carries out world-class research into responsible machine learning.