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Issue #34
This week in Issue #34:
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An extensive and interesting deep dive into the AutoML ecosystem, together with the techniques, tools and challenges that this area in machine learning will face, including end-to-end pipelines, interpretability, reproducibility and beyond.
O'Reilly's Ben Lorica and Yishay Carmiel have put together a 101 for building voice applications. They break down the voice applications into dialogue vs monologues and human2human vs human2machine, and into some of the challenges, applications, and potential.
A really exciting new open source framework brings industrial NLP functionality for text search. GNES [jee-nes] is a cloud-native semantic search system based on deep neural network. It enables large-scale index and semantic search for text-to-text, image-to-image, video-to-video and any content form. Certainly an exciting space for content search at massive scale.
In machine learning adversarial attacks are becoming an increasing worry in production-level applications. This interesting article proposes that adversarial images aren’t 100% a problem—they’re an opportunity to explore new ways of interacting with AI. This is an interesting space, as some of the work we have been doing has provided some correlations around explainability/interpretability techniques in AI, and adversarial attacks, where the ultimate objective is to reverse-engineer models (with different ultimate outcomes of course).
An engineer approach into a data science challenge - Rick Lamers has built a python-first open source spreadsheet application. In this blog post he shows how you are able to leverage the full power of the spreadsheets, together with functionality that python makes available, such as scraping, performing pre-/post-processing, etc. Really interesting project which does seem to offer quite a lot of potential for expansion.
OSS: NN Architecture AutoML
The theme for this week's featured ML libraries is Neural Network Architecture AutoML, which you can find in our Production Machine Learning ecosystem list. These libraries are an incredibly exciting addition that fall in our Responsible ML Principle #4. The four featured libraries this week are:
  • Neural Network Intelligence - NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments.
  • Autokeras - AutoML library for Keras based on "Auto-Keras: Efficient Neural Architecture Search with Network Morphism".
  • ENAS-PyTorch - Efficient Neural Architecture Search (ENAS) in PyTorch based on this paper.
  • Neural Architecture Search with Controller RNN - Basic implementation of Controller RNN from Neural Architecture Search with Reinforcement Learning and Learning Transferable Architectures for Scalable Image Recognition.
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.
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.
Leadership Opportunities
Mid-level Opportunities
Junior Opportunities
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