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Issue #155
This week in Issue #155:
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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!
The NeurIPS 2021 Conference is kicking off this week 🎉 Join us this Tuesday at the NeurIPS LXAI Workshop where we will be giving the opening keynote on "Meditations on First Deployment: A Practical Guide to Responsible AI" 🚀
As the year end approaches we have the chance to jump into the many advent-of-code-like challenges. This is a fantastic resource for ML practitioners to brush up their data science & engineering skills by taking a range of very well (and creatively) prepared set of data challenges.
The discourse for the rise of the end-to-end canonical ML stack continues to evolve. Here is another resource that provides a really interesting combination of open source tooling that aims to cover all the ML model lifecycle through scalable and integrated infrastructure.
A fantastic resource for MLOps practitioners looking to adopt Kubernetes. This free resource provides essential Kubernetes knowledge, and provides the foundation for practitioners to get started with the important and growing cloud native ecosystem.
The O'Reilly team comes back with a fantastic compilation of resources that highlight key trends in the areas of Machine Learning, Programming, Web, VR, Quantum and much more.
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
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