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Issue #128
This week in Issue #128:
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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 Open Data Science Conference (ODSC) team have put together a great overview of "Europe Research Labs to Watch", and we are thrilled that they have featured The Institute for Ethical AI & ML, together with other great academic and industry research labs.
An interesting introspective post from Linkedin analysing and reviewing what Responsible AI means internally. In this post they cover the areas of Fairness and Privacy as well as the road ahead.
Lessons from Netflix, Spotify, etc.
This article analyses the end to end lifecycle of ML and Data, as well as the approach & tools used by leading tech organisations including Netflix, Intuit, Intel, etc. covering the areas of model registries, feature stores, workflow orchestration, serving and monitoring, also showcasing that most of the tools used include a combination of in-house systems with open source frameworks.
A fascinating post from Twitter Engineering around how they developed a recommendation engine to annotate legacy datasets to a new standardized taxonomy. This is very relevant as the importance of metadata grows across organisations, which often comes with manual labor required to annotate the required metadata retrospectively.
An interesting conceptual review of the roles and their responsibilities in organisations building ML & Data systems. In this post it is proposed that the three key roles involve the Data Warehouse Engineers, Data Science Analysis and Data Scientists (which encompasses DS and MLOps / DevOps).
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