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Issue #86
We've hit a significant milestone this week 🚀 Our Production ML Repository has reached over 5000 stars, with exactly 100 closed PRs from over 60 contributors 🔥🔥🔥 We have also reached over 3500 subscribers! Massive thank you to all our community members and Institute Fellows for all your support ✨👏🎉😃 #LetsKeepDoingThis
This week in Issue #86:
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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!
As AI technology is adopted across broader areas in our society, it becomes increasingly important to ensure these systems are aligned with best practices. Individuals often find that economies tend to optimize for metrics that are sometimes not aligned with ethical or societal interests, and hence there is a role for standardisation and regulation of AI. In this article IEML Chief Scientist Alejandro Saucedo provides an insight on the motivations and drivers for "appropriate" regulation in around AI systems as a catalyst for long-term longevity of positive impacts in AI systems in society.
Machine learning delivery teams require a broad set of cross functional skillsets in order to achieve success. The role of AI specialised Product Managers becomes more important in the machine learning productisation lifecycle. In this article, the O'Reilly team breaks down the role of the AI product manager, together with core motivations, responsibilities and recommendations.
Data observability is a common concept in production software systems, however recently there has been an increase in use of this term in the machine learning space. WIth the rise of data availabilty requirements and the increasing complexity of the AI stack, observability has emerged as a critical concern for data teams. This article provides a high level overview of observability in data systems.
The data exchange podcast delves into conversation with Chief Data Officer at DataStax specifically in regards to how graph technologies are being used to solve complex business problems. They also delve into Denise's new book, the practitioner's guide to graph data, and dive into tools and techniques needed to utilise graph technologies in production applications.
The team behind the CleanRL framework have put together a post outlining the benchmark they have proposed and used to evaluate their different reinforcement learning algorithms. This is a great contribution as it falls under the general momentum towards reproducibility and transparency for new methods and libraries deployed, which can give users more granular and accurate insights on where each different algorithm can excel on.
The topic for this week's featured production machine learning libraries is Model Serving 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:
  • 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