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Issue #82
This week in Issue #82:
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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!
A fantastic resource which covers end to end concepts in productionisation of machine learning systems, taught by experts in the field. They have compiled insights focused around formulating the problem, estimating costs, finding & cleaning datasets, picking the right frameworks, assessing compute infrastructure, ensuring reproducibility, troubleshooting training and deploying models at scale.
Many production machine learning challenges are analogous to that of software engineering; this article puts together a high level overview of key insights that software engineers can bring to machine learning. This article dives into reproducibility as version control, model serving as devops and model drift as performance monitoring.
A very interesting evaluation of machine learning performance comparing rest vs kafka APIs for the usecase of streaming data. In this article we can see Playtica's journey assessing a benchmarking of the ETL systems (such as Airflow) vs streaming systems (such as Kafka), and how they compare in service exhaustion, client starvation, handling failures, retries and performance. has announced a new OSS project in their data version control family, called continuous machine learning (CML). This CML framework dives into CI/CD for machine learning, introducing best practices for continuous delivery for model training, model evaluation, comparing ML experiments, and monitoring dataset changes.
The great ML resource PapersWithCode has released a new feature called "Methods". Here they are now tracking 730+ building blocks of machine learning: optimizers, activations, attention layers, convolutions and much more. This allows the community to track usage over time and explore papers from a new perspective.
[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. 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