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Issue #142
This week in Issue #142:
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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 submission for the European Commission consultation on the Foundational Principles for Digital Citizenship has now been published 🚀 This contribution includes comments that emphasise the importance of universal access to internet & services, ethical principles for human-centric algorithms and a broad range of other critical topics.
ApplyingML has put together a set of compiled interviews with seasoned practitioners in the machine learning space, including senior, staff and director level experiences across tech companies like Amazon, Databricks, Spotify and more.
A comprehensive article that provides a deep dive into concept drift techniques, as well as best practices on how to combat the divergence between static models and dynamic environments.
A great resource that provides best practices around software design for data scientists, and outlines the motivations, principles and hands on examples for the various concepts it introduces.
The rise of DataOps has emphasised the importance of data quality as opposed to just quantity. This article provides an overview of some core concepts of data quality, but also the reasoning for the growing ecosystem of solutions to address these challenges.
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