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Issue #29
Our "Awesome Production Machine Learning" list has reached over 700 stars and our AI explainability library has reached over 200 🎉 thanks to everyone for your support! Let's continue exploring the challenges and opportunities of production ML 🚀
This week in Issue #29:
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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!
Excellent eagle-eyed view of the major trends in AI and Data in 2019.  This article is a two-part article that dives into both the infrastructure and the higher level strategic trends. This article in particular is the Part 2, which covers some of the critical topics we dive into every week, including ML Orchestration, serverless, data governance, data catalogs, lineage and beyond.
We have put together an end-to-end tutorial to showcase how to deploy a production ML model, and then leverage some of the black box model AI interpretation techniques in the Alibi library to provide an interface for production-level explanations. This design pattern allows you to deploy a black box income classifier model together with another model running in parallel that is able to explain why that initial model made the predicctions it has made. The technique used to reverse-engineer and interpret preictions is "Anchor explanations" which basically answers the question of "what are the features in this inference request that influenced the prediction the most?".
Excellent (albeit brief) article outlining the failure rate of big data (hadoop-related) projects. With a staggering 85%, it is clear how important it is to make sure the success criteria is well set from the beginning to avoid failure of projects. Although it is necessary to ensure companies are able to run internal POCs (proof-of-concepts or Pilots) , it is also critical to make sure that the path is planned for the organisation to be able to support production-ready adoptions of these big data projects.
Machine learning mastery comes back this week with an excellent tutorial that dives into GANs, and covers three key areas: 1) How to define and train the standalone discriminator model for learning the difference between real and fake images. 2) How to define the standalone generator model and train the composite generator and discriminator model. 3)How to evaluate the performance of the GAN and use the final standalone generator model to generate new images.
Intel Software Innovator Daniel Whitenack has put together an awesome production-level framework with modular functionality to perform question-answering ML inference on top of Kubernetes. They've put toether a brief screencast that showcase how you can interact with it, as well as a Arxiv research paper with full details on the framework.
OSS: Adversarial Robustness
The theme for this week's featured ML libraries is once again Adversarial Robustness, which includes tools for adversarial attacks and adversarial security. These libraries are an incredibly exciting addition that fall in our Responsible ML Principle #8, and the whole section was contributed by one of the Fellows at the Institute Ilja Moisejevs from Calipso AI. The four featured libraries this week are:
  • AdverTorch - library for adversarial attacks / defenses specifically for PyTorch.
  •  TextFool - plausible looking adversarial examples for text generation.
  • DEEPSEC - another systematic tool for attacking and defending deep learning models.
  • Artificial Adversary - AirBnB's library to generate text that reads the same to a human but passes adversarial classifiers.
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
© 2018 The Institute for Ethical AI & Machine Learning