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THE ML ENGINEER 🤖
Issue #79
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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 a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
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Demands for large scale production machine learning capabilities are growing at breakneck speeds, and the ecosystem of tools are growing at equally fast pace. DKB ML Lead Engineer Lina Weichbrodt has made a fantastic contribution to our OSS Production ML Tools list, adding a new section with tools that specialise on large-scale frameworks for ML serving and monitoring. This is a great new addition which we're quite excited about as it will allow the community to stay up to date with innovation in this field.
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Machine Learning Operations (or MLOps) enables Data Scientists to work in a more collaborative fashion, by providing testing, lineage, versioning, and historical information in an automated way. GitHub has put together an article that outlines how it's possible to leverage the GitHub Actions feature that integrates parts of the data science and machine learning workflow with a software development workflow.
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The Data Exchange podcast dives into conversation with SpaCy and ExplosionAI Cofounder Matt Honnibal. In this great episode Matt shares insights related to the most popular NLP library SpaCy, together with some of the other fantastic projects the ExplosionAI team is working on including the ML framework Thinc, their commercial data labelling tool Prodi.gy and beyond.
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In recent years machine learning research – particularly research in deep learning – has had a profound impact on enterprise applications. We’re now also seeing more researchers studying RL and some of these investments will begin to show up in applications. In this post Chief Data Scientist Ben Lorica dives into enterprise applications of reinforcement learning, together with insightful metrics and facts of adoption in industry.
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Transfer learning has proven to be a successful technique to train deep learning models in the domains where little training data is available. The dominant approach is to pretrain a model on a large generic dataset such as ImageNet and finetune its weights on the target domain. This fascinating paper proposes an architecture for a large scale neural transfer search framework, together with a SaaS implementation of the service which can be tested.
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[Updated List 21/06/2020] 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 a@ethical.institute
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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
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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
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© 2018 The Institute for Ethical AI & Machine Learning
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