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THE ML ENGINEER 🤖
Issue #131
 
 
This week in Issue #131:
 
 
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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 a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
 
 
 
A brand new course from HuggingFace which dives into Natural Language Processing with the libraries and frameworks in their ecosystem, and covers the full spectrum of transformers, datasets, tokenizers and more.
 
 
 
A great resources that maps the ML tooling ecosystem and provides a visualisation and overview of the top tools in the end to end machine learning lifecycle, as well as their "coverage" of features compared side-by-side.
 
 
 
McKinsey digital has produced a report that explores the technologies they identify having the most momentum in the fast-growing tech world, and provides a taxonomy and intuition for these technologies.
 
 
 
An interesting talk from Feature Engineering Company Tecton which covers lessons learned adopting and then moving away from Apache Kafka, reminding us how popular frameworks have their time, place and context.
 
 
 
This article proposes the need for specialisation on database architectures to support optimal interoperability with vector embeddings. This post provides a great overview on the growing popularity of embeddings in machine learning, as well as their generalisation across a broad range of applications, and the need for specialised architectures to get the most out of them at scale.
 
 
 
 
 
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