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Issue #26
 This week we will be speaking at the AI O'Reilly Beijing on Machine Learning Explainability (XAI), and next week we'll be speaking on Machine Learning Orchestration at Kubecon Shanghai, Open Source Summit, OSCon and Slush China ๐Ÿš€ Come say hello!
This week in Issue #26:
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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!
Reproducibility is an essential requirement for many fields of research including those based on machine learning techniques. PyTorch has released PyTorch Hub, where the community can now share models built with PyTorch. This new great resource also has built-in support for Colab, integration with Papers With Code and currently contains a broad set of models that include Classification and Segmentation, Generative, Transformers, and beyond ๐Ÿš€.
What a time to be alive for life-long learners - a brand new Free online course has been made available by Facebook AI on hands down some of the most exciting topics in this space: Federated Learning, Differential Privacy and Encrypted computation. This course teaches you how to leverage open source tools to explore these topics on an introductory level. Really awesome to see this type of content be made available freely.
MLflow from Databricks is an open source framework that addresses some of the biggest challenges in machine learning, including configuring environments, tracking experiments, and deploying trained models for inference. This post provides a high level overview on this framework as well as useful links to get started trying it out.
End to end pipelines are always a challenge in the data science space. Kubeflow is an open source framework that hells you run reproducible ML workloads in Kubernetes. This example showcases and end-to-end NLP pipeline leveraging re-usable components that utilize key frameworks such as the SpaCy NLP library to perform automation of text analysis, as well as serving the models using Seldon.
The DataHack team has put together a great podcast where they bring the co-founders of, and authors of SpaCy to talk about the story behind this popular framework. During this 40 minute episode, they dive into the idea behind developing spaCy, spaCyโ€™s evolution from the first alpha release, use cases of spaCy including a couple of surprising applicationsInes, and Mattโ€™s advice to NLP enthusiasts.
MLOps = Featured OS Libraries
The theme for this week's featured ML libraries is ML Model and Data Versioning frameworks, which fall on our Responsible ML Principle #4. The four featured libraries this week are:
  • Data Version Control (DVC) - A git-like framework that allows for version management of models
  • Pachyderm - Open source distributed processing framework build on Kubernetes focused mainly on dynamic building of production machine learning pipelines
  • ModelDB - Framework to track all the steps in your ML code to keep track of what version of your model obtained which accuracy, and then visualise it and query it via the UI
  • PredictionIO - An open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task
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
  • AI Conference Beijing [18/06/2019] - O'Reilly's signature applied AI conference in Asia in Beijing, China.
  • 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.
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!
Leadership Opportunities
Mid-level Opportunities
Junior Opportunities
ยฉ 2018 The Institute for Ethical AI & Machine Learning