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Issue #119
This week in Issue #119:
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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!
A great overview of MLOps by Andrew Ng. This talk provides a foundational intuition coming from the core machine learning principles, and introduces the challenges around deployed models such as re-training, bias/variance, and general DevOps / ML Engineering challenges.
Facebook AI PM Elivs has put together a list of machine learning and deep learning courses which contain extensive content spanning across a broad range of ML areas.
Arize AI Co-founder Aparna Dhinakaran has put together a playbook that outlines the challenges in prod ML connected to availability of ground truth with deployed models, as well as the metrics available that can be used for each scenario.
A fascinating resource that provides a hands on intuition of the internals of databases by guiding us through the development of a simple database from scratch using the C language. It covers foundational concepts such as data saving formats, moving memory, primary keys, transaction rollbacks and more.
Data Scientist Or Itzary shares a conceptual framework that enable practitioners to assess when models should be retrained, as well as the data that should be used in the context of production deployments.
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