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Issue #169
THE ML ENGINEER 🤖
 
This #169 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 🚀
 
If you like the content please support the newsletter by sharing on 🐦 Twitter,  💼 Linkedin and  📕 Facebook!
 
 
 
 
This week in Issue #169:
 
 
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!
 
 
 
An interesting strategic market analysis of the MLOps ecosystem from a VC firm that proposes that the current trend for technologies is from the e2e platforms towards the best-of-breed open core and SaaS products.
 
 
The O'Reilly team has once again put together a fantastic compilation of key trends across various areas of the technology space, including AI, programming, security, web and much more.
 
 
An interesting article outlining key insights and lessons learned from applying kubernetes architectural patterns into the domain of declarative and distrbuted deep learning model training, enabling for elastic infrastructure scaling for AutoML in an automated manner.
 
 
An interesting paper from Google showcasing their most recent multi-domain language model which aims to provide for a variety of tasks including translation, summarisation, question answering and more, whilst aiming to abide by higher level Responsible AI principles.
 
 
This article provides a high level overview as well as a set of key trends for the cloud security ecosystem in 2022, ranging across the development stack, remediation processes, pipelines, security resources and even machine learning.
 
 
 
 
 
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