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Issue #99
This week in Issue #99:
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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!
Deployed production models require domain specific monitoring capabilities. This article provides an insight on the topic of ML monitoring, including performance metrics, behavioural metrics, feature behaviour, metadata and inference data.
The Data Exchange Podcast comes back this week with a conversation with CSAIL Lab Research Scientist Neil Thompson, where they discuss his recent paper "The Computational Limits of Deep Learning".
Safely rolling out ML models to production is key in production. This article provides an overview of the lifecycle of a model, together with the key components that can improve the stability and robustness of production models at scale.
Spotify has open sourced Kilo, their framework for building data pipelines for audio and media processing based on Python and Apache Beam. In this article they dive into the core architectural and domain-specific principles.
Great article that discusses how to set up an efficient environment for machine learning on Kubernetes. The article covers some of the common architectural bottlenecks to avoid, as well as best practices in machine learning using kubernetes terminology (and vice-versa).
The topic for this week's featured production machine learning libraries is Industry 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:
  • SpaCy - Industrial-strength natural language processing library built with python and cython by the team.
  • Snorkel - Snorkel is a system for quickly generating training data with weak supervision
  • Transformers - Huggingface's library of state-of-the-art pretrained models for Natural Language Processing (NLP).
  • 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 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:
  • ACM's Code of Ethics and Professional Cond uct - 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
About us
The Institute for Ethical AI & Machine Learning is a Europe-based research centre that carries out world-class research into responsible machine learning.
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