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Issue #123
This week in Issue #123:
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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!
Uber Engineers Yupeng Fu and Chinmay Soman share key insights on Uber's data infrastructure in a recent whitepaper - they cover the overall architecture of the real time infrastructure and identify scaling challenges that they need to continuously address for each component in the architecture.
CNCF's VP Ecosystem Cheryl Hung interviews CERN computing engineer Ricardo Rocha and delves into the scale of CERN's infrastructure managing 600 clusters.
A fascinating application of OpenAPI's GPT3 API showcasing how to leverage these models specifically for automatic text to bash-scripts applications. This provides an intuition of the potential of these emerging technologies, but also the importance of human in the loop, in this case to avoid an accidental "sudo rm -rf /".
Security in the MLOps lifecycle is key - Ian Hellstrom shares in a latest blog post some key principles for Development Security Operations for Machine Learning, or DevSecOps for ML. This article dives into vulnerability scalling and benefits of distroless images.
Python 3.10 Feature Highlights
A great overview of key features coming in with Python 3.10, including type checking improvements, type aliases syntax, population count, context manager syntax, and 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:
  • 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