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Issue #78
This week in Issue #78:
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The demand for outlier & anomaly detection techniques in production machine learning systems has seen a fast adoption. With this there has been a large number of new tools tackling these challenges. INNOQ Machine Learning Engineer Larysa Visengeriyeva has made a great contribution to our OSS Production ML Tools list, adding a new section with tools that specialise on outlier & anomaly detection. This is a fantastic new addition which we're quite excited about as it will allow the community to stay up to date with innovation in this field.
Python continues to be the fastest growing language for scientific computing, data science and machine learning. Sebastian Raschka has put together together with Joshua Patterson and Corey Nolet an overview of the current state of machine learning in python, diving into some of the main developments and technology trends in data science, machine learning and broader artificial intelligence.
Homomorphic Encryption is a fascinating privacy preserving machine learning technique that allows for processing to take place on encrypted data, which provides the same results as if the computation was processed on the plaintext. This of course comes at a computational cost, however the developments in these techniques are making them more accessible. In this talk Hao Chen from Microsoft Research dives into some of the practical applications of this techqnique, together with an overview of the technique itself.
We covered last week the launch of the new OpenAPI GPT3 release, a model that requires an unprecedented amount of computational power to even process an inference, let alone train. This week OpenAPI has released a new commercial API for NLP tasks including semantic search, summarization, sentiment analysis, content generation, translation, and more.
With the demands for large scale production machine learning systems, the skills required to build, maintain and operate these systems require a set of cross-functional skills which range from data science to devops. In the context of the devops requirements, the trend of continuous delivery has become growingly important with the emergence and adoption of frameworks like Kubernetes. The Continuous Delivery foundation has released an exciting initiative to dive into some of the topics that are critical in MLOps in their new CI/CD & DevOps Podcasts. Check it out.
[Updated List 14/06/2020] Due to the current global situation, a large number of conferences have had to face hard choices, several which decided going fully virtual. This hard choice has now open the doors to people from around the world to gain access to the great online content generated by expert speakers and contributors. We wanted to highlight some of these key conferences so they are not missed - these include:
Did we miss any? Please let us know by replying to the newsletter email or by simply emailing us at
The topic for this week's featured production machine learning libraries is Outlier Detection - massive shoutout to INNOQ Machine Learning Engineer Larysa Visengeriyeva for contributing this section to the production ML list. 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:
  • adtk - A Python toolkit for rule-based/unsupervised anomaly detection in time series.
  • Alibi-Detect - Algorithms for outlier and adversarial instance detection, concept drift and metrics.
  • Deequ - A library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
  • PyOD - A Python Toolbox for Scalable Outlier Detection (Anomaly Detection).
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
© 2018 The Institute for Ethical AI & Machine Learning