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Issue #165
THE ML ENGINEER 🤖
 
 This #165 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 #165:
 
 
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!
 
 
 
The AI Ethics Meetup comes back with an online event where "Machine Ethics Podcast" founder shares his thoughts reflecting on 6 years of running the podcast, providing insights around how the conversation and ecosystem has evolved throughout.
 
 
ML Streaming Platform founder Chip Huyen has put together a fantastic resource which is part of the new Stanford course on Machine Learning Systems Design focusing on data distribution shifts and relevant monitoring themes.
 
 
The O'Reilly team has put together a fantastic summary of key trends and developments for the start of the year, covering Machine Learning, general development, security, automation, infrastructure and more.
 
 
As the field of MLOps continues to mature, so does its definition. This article provides a great revisit to the term of MLOps including the sub-themes it encompasses, as well as a definition and examples of each of these.
 
 
Yotpo Tech Leader Elia Rohana has put together an interesting piece summarising lessons learned building scalable real time infrastructure for large scale data and events processing, including the architectural details and some of the frameworks that were explored and then used.
 
 
 
 
 
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