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Issue #81
This week in Issue #81:
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A fantastic and comprehensible post by Automaticc Machine Learning Engineer Vicki Boykis covering an end to end journey towards productionising an AI powered application. This great post provides a sneak-peek into some of the challenges and pain-points involved when developing some of the underlying components required to produce production ready machine learning services, which are able to power an AI application.
Machine Learning Mastery's Jason Brownlee has put together a great post featuring 8 of the top books on data cleaning and feature engineering as recommended reads. Feature engineering is key in the machine learning lifecycle, as it enables for better performance, more robust moedls, more explainable models (through domain knowledge abstraction), between other improvements.
Micosoft Researches have published a research survey that provides insights on the state of adversarial ML in industry, through 28 interviews which outlines key insighs on the gaps in securing machine learning systems when viewed from the context of traditional software security development. This paper provides a deep dive from the perspective of both ML engineers and security incident responders, making it quite an interesting piece for practitioners involved in the development and design of production machine learning systems.
Table information extraction in natural language processing is a well known and still not fully resolved challenge across both research and industry. Google has released an article that aims to showcase their achievements tacking this challenge by leveraging state-of-the-art NLP deep learning frameworks. This article provides both theoretical and practical insights on "TAPAS", a weakly supervised table parsing approach that extends the BERT architecture to tackle this challenge via question answering techniques on (seemlingly structured) text-based tables.
Why should you care about Causal Inference? Most, if not all, business analytics questions, are inquiries of cause and effect. This fantastic article provides an introductory insight into causal inference with practical and intuitive examples. It also aims to provide an intuition on when this branch of techniques are "good enough" as well as more importantly "when they are not".
[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
About us
The Institute for Ethical AI & Machine Learning is a UK-based research centre that carries out world-class research into responsible machine learning.
© 2018 The Institute for Ethical AI & Machine Learning