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Issue #9

This week in Issue #9:
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Chief Scientist at the Institute for Ethical AI & ML Alejandro Saucedo covers multiple tools available to scale your production machine learning in a short talk at FOSDEM 2019. In this talk, Alejandro covers open source machine learning frameworks in explainability, model & data versioning and orchestration including DVC, Seldon, Pachyderm, CombustML, Algorithmia, SHAP, and more.
Great post by the Seldon team on anomaly/outlier detection using machine learning for machine learning models. The post provides background on the different types of outlier detection, as well as the typical approaches used to identify them. It goes into detail on 4 implemented algorithms, namely sequence to sequence LSTMs, variational auto-encoders, isolation forests and mahalanobis distance.
A high level overview that tackles the motivations, challenges and solutions for scaling the machine learning capabilities at Facebook. The talk also references a great talk from Facebook at a machine learning conference where they talk about their ML infrastructure.
Jason from Machine Learning Mastery brings us an excellent tutorial on transfer learning. The article covers the definition of transfer learning, and a hands on example on a multi-class classificaiton problem using a neural network and comparing multiple models
Fascinating paper by Google on Federated Learning - a method that allows for machine learning models to be trained across multiple edge devices in the network instead on a central server. This means that models can be trained from mobile phones or laptops without requiring the data to be transferred to a central server. This is really exciting as it could have very positive impact on data privacy. Gavin Belson would be proud.
For anyone looking to jump on the quantum hype train, the University of Toronto has launched a course on quantum machine learning. Although there's no blockchain on this course (at least for now), taking on such a nieche intersection it has surely managed to get noticed by the community, certainly with a lot of mixed opinion.
We are excited to see the Awesome MLOps list growing to almost 300 stars now! Thanks to everyone for your support! This week's edition is focused on industrial strength visualisation frameworks which fall on our Responsible ML Principle #5. The four featured libraries this week are:
  • - An interactive, open source, and browser-based graphing library for Python.
  • Pixiedust - PixieDust is a productivity tool for Python or Scala notebooks, which lets a developer encapsulate business logic into something easy for your customers to consume.
  • ggplot2 - An implementation of the grammar of graphics for python.
  • seaborn - Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
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 showcase Machine Learning Engineering jobs (primarily in London for now) to help our community stay up to date with great opportunities that come up. It seems that the demand for data scientists continues to rise!
Junior Opportunities
Mid-level Opportunities
Leadership Opportunities
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 Conferences
  • AI Conference Beijing [18/06/2019] - O'Reilly's signature applied AI conference in Asia in Beijing, China.
Business Conferences
© 2018 The Institute for Ethical AI & Machine Learning