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Issue #170
This #170 edition of the ML Engineer newsletter contains curated articles, tutorials and blog posts from experienced Machine Learning and MLOps professionals. You can access the Web Newsletter Homepage as well as the Linkedin Newsletter Homepage where you can find all previous editions 🚀
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This week in Issue #170:
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 London MLOps meetup comes back this week with two fantastic talks from TolokaAI and MeshAI, and will be delivered by experienced practitioners in the production machine learning operations space. Join us this Tuesday in-person in London or virtually through he livestream.
Fascinating article from Andrej Karpathy where he attempts to reproduce Yann LeCun's 1989 paper on backprop, achieving a 90 second training on a modern laptop compared to the many days it took back then, sharing a lot of interesting retrospective insights on machine learning then and now.
The annual AI Index Report from Stanford University's Human Centered Artificial Intelligence lab has come out. A lot of fantastic insights, including investments in the AI space doubling to $93b+ and counting, as well as interesting geopolitical, technological and societal trends.
An interesting effort from Microsoft Research showcasing how they have tackled the chalenge of varied computational footprints of modern ML algorithms when reaching large scale for data processing, namely through a microservice-based service architecture running in spark-based infrastructure, shocasing benchmarks and practical applications.
A fantastic and growingly popular resource for practitioners to learn the foundations and best practices of all-things-kubernetes with a hands on tutorial, which is recommended for anyone intrested to learn about the internals of kubernetes beyond the application and use of this powerful framework.
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:
  • 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