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Issue #47
This week in Issue #47:
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Machine Learning Engineer Adrian Gonzales has put together a fantastic hands on tutorial which covers the motivations, challenges and best practices when setting up end-to-end machine learning systems. In this talk he dives into how MLFlow can be leveraged to train models through their experimentation functionality, and leverage the Seldon-MLFlow integation to seamlessly deploy models into a production Kubernetes cluster, where he then runs real-time feedback analysis whilst A/B testing the models. You can watch the video in youtube and try it out yourself with the jupyter notebook.
Neurobotics is leveraging machine learning to reverse engineer human thoughts. They have come up with quite a clever way to leverage labelled data through the expected visual appearance of an object linked to the brain waves that would be emitted when perceiving it. This startup has very interesting showcases of their technology that provide very interesting initial results. Technology applications like this can be incredibly interestibg, but also require an evaluation of its impact in society.
Machine learning experimentation can be highly time consuming, and with growing complexity of machine learning requirements make it harder to build and run experimentation at scale. The team at Intel have put together a great insight on how they are tackling it with their "analytics zoo", and more specifically in this post they outline how it can be tackled leveraging AutoML using Ray on top of Spark. This is an interesting approach as it allows the technical user to leverage existing Spark infrastructure, but with the simplicity of Ray, which in this case allows for large scale automated hyperparameter search.
O'Reilly and TensorFlow teamed up to put together the first TensorFlow World conference, which took place recently. It brought together the growing TensorFlow community to learn from each other and explore new ideas, techniques, and approaches in deep and machine learning. The videos for the conference are now live in YouTube.
Machine learning is a very broad subject. Machine learning mastery does a fantastic job to map out some of the key different types of learning in machine learning. These include an intuitive overview of what these consist of, and include learning problems, hybrid learning problems, statistical inference and learning techniques.
The theme for this week's featured ML libraries is Data Science Notebooks, and we're happy to share brand new libraries into that section to showcase tools beyond the good old Jupyter Notebooks. The four featured libraries this week are:
  • ML Workspace - All-in-one web IDE for machine learning and data science. Combines Jupyter, VS Code, Tensorflow, and many other tools/libraries into one Docker image.
  • Polynote - Polynote is an experimental polyglot notebook environment. Currently, it supports Scala and Python (with or without Spark), SQL, and Vega.
  • Stencila - Stencila is a platform for creating, collaborating on, and sharing data driven content. Content that is transparent and reproducible.
  • RMarkdown - The rmarkdown package is a next generation implementation of R Markdown based on Pandoc.
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 showcasing three resources from our list so we can check them out every week. This week's resources are:
  • AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems.
  • Ethics Canvas - A resource inspired by the traditional business canvas, which provides an interactive way to brainstorm potential risks, opportunities and solutions to ethical challenges that may be faced in a project using post-it note-like approach.
  • ISO/IEC's Standards for Artificial Intelligence - The ISO's initiative for Artificial Intelligence standards, which include a large set of subsequent standards ranging across Big Data, AI Terminology, Machine Learning frameworks, etc.
  • GDPR.EU Guide - A project co-funded by the Horizon 2020 Framework programme of the EU which provides a resource for organisations and individuals researching GDPR, including a library of straightforward and up-to-date information to help organisations achieve GDPR compliance (Legal Text).
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
We feature conferences that have core  ML tracks (primarily in Europe for now) to help our community stay up to date with great events coming up.
Technical & Scientific Conferences
  • Data Natives [21/11/2019] - Data conference in Berlin, Germany.
  • ODSC Europe [19/11/2019] - The Open Data Science Conference in  London, UK.
Business Conferences
  • Big Data LDN 2019 [13/11/2019] - Conference for strategy and tech on big data in London, UK.
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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© 2018 The Institute for Ethical AI & Machine Learning