The MachineLearning EngineerIssue #118

This week in Issue #118:

• Visualizing Timeliness at Airbnb
• Video Calls on Steroids with NVIDIA
• Break a Prod Model in 20 Days
• The Annual 2021 AI Index
• The Next Generation of AI
• 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!

Visualizing Timeliness at Airbnb

An interesting post from Airbnb Engineering that delves into the concept of “Dataset Creation/Updating SLAs”, including approaches to identify and manage the dependencies across datasets, as well as the best practices and approaches to ensure bottlenecks and risks are identified and addressed to minimise out-of-date/unavailable datasets when they are required.

Video Calls on Steroids w NVIDIA

A fascinating piece of research by Nvidia where they showcase an approach to achieve higher quality video transfer with significantly lower data transfer rates. This method takes an innovative approach transferring only an initial picture and streaming information about the head position and metadata of expressions, achieving impressive results - and also opening a new frontier in privacy considerations given the metadata that will be extracted from individuals.

Break a Prod Model in 20 Days

A tutorial that provides a practical example of performance divergence for production models, using a case study with a simple random forest model trained on the Kaggle bike sharing demand dataset.

The Annual 2021 AI Index

The AI Index this year showcases interesting insights about the state of AI in research and industry, and showcases major growth despite the pandemic, including a maturing industry, significant private investment, and rising competition between China and the US.

The Next Generation of AI

The O’Reilly team explores the interesting trends in the intersection of emerging areas in artificial intelligence to identify the next big trends, and also delves into some of the key challenges both technical and ethical in the current state of AI.

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!