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Issue #138
This week in Issue #138:
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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! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
Our consultation submission for the European Commission's AI Regulation Proposal has now been published! We are thrilled to have worked with such a fantastic team of thought leaders with the Association for Computing Machinery to contribute to such an important topic 🚀 In the submission we outline the need to better protect public health, safety, & privacy + leverage universal Informatics education to close "skills gap" with a diverse, AI-literate workforce.
Insightful article written by contributors from Ludwig and Overton, where they share their insights on motivations, concepts and learnings on the higher level topic they define as declarative machine learning systems.
Andrew Ng started creating resources around MLOps including the online Coursera course. This week they are hosting a panel event where they will be diving deeper into what they refer as the area of data centric MLOps.
An insightful article which is part of a longer MLOps series, which dives into feature distribution stores, and compares popular solutions such as Google Cloud services and open source frameworks like FEAST.
Practitioners that are looking to enhance their core knowledge will be able to leverage the recently released book "An introduction to statistical learning", which is available for free online as PDF version.
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
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:
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!
© 2018 The Institute for Ethical AI & Machine Learning