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Issue #80
This week in Issue #80:
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Great deep dive into Explainability in AI, which is a key component in production machine learning systems. In this article they dive into how Machine learning algorithms are increasingly being used in high stakes decisions, steps to mitigate the risk of these black-box algorithms, a number of explainability techniques, a great collection of resources representing the application of explainability methods in a practical setting, and key insights on challenges applying these methods in the field.
Last week at the Spark + AI Summit 2020 Databricks announced that their flagship open source AI framework MLFlow is becoming a Linux Foundation project! This is absolutely fantastic news for the open souce and enterprise machine learning ecosystem as it will further the current topic of experiment management and deployment lifecycle. During this conference they also announced some core roadmap features that will be added into the MLFlow library, together with some of the plans and stats behind this great decision.
The Data Version Control (DVC) framework has released their 1.0 version! This is a great announcement for the MLOps ecosystem as this is one of the core tools providing full provenance and version control to machine learning assets, introducing sophisticated versioning capabilities for the machine learning constitutens of each pipeline component, consisting of data, config and code.
The Data Exchange Podcast comes back this week with a conversation with Christopher Nguyen, CEO of Arimo (a Panasonic company). Christopher is a former Engineering Director at Google, and was an early proponent of deep learning for enterprise applications. In this podcast they dive into the difference between working at an AI vendor company vs working at a AI buying company. They also dive into ML usecases for IoT and Industrial internet apps, and also cover key concepts in MLOps. is new resource in the MLOps space covering some of the core principles on the topic of productionisation of machine learning across its full lifecycle. This resource includes a concise definition of MLOps, together with several deep dives into sub-topics of MLOps, including underlying motivations, design processes, workflows, principles and more.
[Updated List 28/06/2020] 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 Model Serving Frameworks - massive shoutout to DKB ML Lead Engineer Lina Weichbrodt 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:
  • KFServing - Serverless framework to deploy machine learning models in Kubernetes with KNative
  • Seldon Core - Open source platform for deploying and monitoring models in kubernetes with rich DAG structures
  • Cortex - Cortex is an open source platform for deploying machine learning models—trained with nearly any framework—as production web services.
  • Tensorflow Serving - High-performant framework to serve Tensorflow models via grpc protocol able to handle 100k requests per second per core
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