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Issue #65
We are proud to announce we have reached over 2500 subscribers for the ML Engineer Newsletter πŸš€ and the open source Production Machine Learning repo has reached 3000 stars πŸ”₯πŸ”₯πŸ”₯ a massive thank you to all our subscribers and community members for all your support βœ¨πŸ‘πŸŽ‰πŸ˜ƒ #LetsKeepDoingThis
This week in Issue #65:
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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! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
The Data Exchange Podcast comes back this week with a conversation on machine learning explainability, MLOps, adversarial robustness and privacy preserving ML, with Institute for Ethical AI Chief Scientist & Seldon Engineering Director Alejandro Saucedo. In this podcast Ben Lorica and Alejandro dive into some of the key trends in machine learning, as well as some of the core best practices for developing, deploying and monitoring production machine learning at massive scale.
A fantastic resource that has been compiled together from some of the best books found through conversations across developers and researchers. Specifically this sub-page has carefully curated Python Books that focus specifically on machine learning. The books referenced in this section are not only great for beginners, but also for intermediate-level ML learners.
What would machine learning look like if you mixed in DevOps? Wonder no more, Seldon Open Source Developer Dr. Ryan Dawson has put together a great piece that outlines the concept of MLOps, together with some of the existing biggest challenges, solutions and best practices, together with some of the initiatives that are advancing these discussions forward.
A fantastic article by Spotify Senior Data Engineer Ian HellstrΓΆm which aims to provide a high level overview of some of the end-to-end platforms available in the MLOps space. This post dives into Google TFX, Uber Michelangelo, Airbnb Bighead, Netflix Metaflow and more.
Another fantastic resource that dives into the broad world of Adversarial Robustness, which has curated and carefully selected pieces that are recommended as reading list for anyone interested to learn more on the topic. This resource also provides a high level overview that covers the basics, a quick introduction, a complete background and papers broken down by various categories.
The topic for this week's featured production machine learning libraries is Industrial Strength NLP. 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:
  • πŸ€— Transformers - Huggingface's library of state-of-the-art pretrained models for Natural Language Processing (NLP).
  • Grover - Grover is a model for Neural Fake News -- both generation and detection. However, it probably can also be used for other generation tasks.
  • Kashgari - Kashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks.
  • Github's Semantic - Github's text library for parsing, analyzing, and comparing source code across many languages .
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 thiese 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. We will be showcasingitg three resources from our list so we can check them out every week. This week's resources are:
  • ACM's Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018
  • From What to How - An initial review of publicly available AI Ethics Tools, Methods and Research to translate principles into practices
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
Β© 2018 The Institute for Ethical AI & Machine Learning