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THE ML ENGINEER 🤖
Issue #115
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This week we are celebrating a big milestone 🥳🎉🥂 The ML Engineering Newsletter has reached over 5000 subscribers!! Our Production ML List has also reached over 8,200 stars on GitHub!! As always we want to thank all our subscribers and members at the Institute for Ethical ML for your great support 🚀🚀🚀🚀
This week in Issue #115:
If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
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The Data Exchange podcast presents an interesting conversation with DarwinAI CoFounders Sheldon Fernandez and Alex Wong, where they dive into the topic of simple, interpretable and trustworthy AI.
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JetBrains has published the results for their 4th official Python Developer Survey in collaboration with the Python Software Foundation. In this interesting edition they showcase results from more than 28k Python devs from almost 200 countries.
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The SpaCy team has published an interesting overview of the release of the 3.0 version of one of the mos popular NLP libraries. They provide an overview of features as well as hands on guides going from prototype to production for NLP usecases.
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Neural MMO is a really interesting initiative that provides a platform for agent-based intelligence research featuring hundreds of concurrent agents, multi-thousand-step time horizons, and procedurally-generated, million-tile maps. The documentation provides an intutivie and practical deep dive into the project.
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An intereting set of lectures that dive into practical insights on applied research around perception algorithms for driverless cars. This has been created through an online meetup and although most of the lectures have been published there are still a couple of events coming up.
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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.
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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:
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© 2018 The Institute for Ethical AI & Machine Learning
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