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Issue #72
This week in Issue #72:
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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!
The lifecycel of a machine learning model only begins when it's deployed to production. It's key to be able to understand the performance, and especially the degrades that the models experience as time passes. Whether it is due to data distribution changing, or other external factors, it's key to ensure the right infrastructure is in place. This article provides an excellent picture of this challenge and various tools and solutions available.
Springer has released hundreds of free books on a wide range of topics to the general public. The list, which includes 408 books in total, covers a wide range of scientific and technological topics. In order to save you some time, this article has created a single list of all the books (65 in number) that are relevant to the data and Machine Learning field.
OpenAI has released a very interesting announcement, the launch of Jukebox, a neural network based model that can be used to generate music of different genres with lyrics. In this post they cover in depth the approach as well as various examples that were generated with this model.
The Data Exchange podcast comes back this week with a deep dive with DeterminedAI CEO Evan Sparks, where they dive into their brand new open sourced Deep Learning Training platform, together with some key enterprise use-cases of deep learning, the challenges and opportunities of distributed training & hyperparameter tuning, as well as some examples of how teams have been using their open source platform.
As we face one of the biggest challenges of our generation, several ethical implications arise which often appear to clash with urgency of proposed solutions - one of the ongoing key technological discussions in this space is the use and approach towards contact tracing techology. We are organising a meetup on the 15th of March where HATLAB Deputy Director wil dive into the ethical implications of responses to COVID in the context of contact tracing in particular.
[Updated List 26/04/2020] 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 Data Visualisation. 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:
  • Microsoft SEAL - Microsoft SEAL is an easy-to-use open-source (MIT licensed) homomorphic encryption library developed by the Cryptography Research group at Microsoft.
  • PySyft - A Python library for secure, private Deep Learning. PySyft decouples private data from model training, using Multi-Party Computation (MPC) within PyTorch.
  • Tensorflow Privacy - A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.
  • TF Encrypted - A Framework for Confidential Machine Learning on Encrypted Data in TensorFlow
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 systems.
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