AI, Machine Learning and Data Science Roundup: February 2019
This article is originally published at https://blog.revolutionanalytics.com/
A monthly roundup of news about Artificial Intelligence, Machine Learning and Data Science. This is an eclectic collection of interesting blog posts, software announcements and data applications from Microsoft and elsewhere that I've noted over the past month or so.
Open Source AI, ML & Data Science News
ONNX, the open interchange format for AI models, updates to version 1.4.0 with support for models larger than 2Gb.
Stanford University has released StanfordNLP, a natural language analysis package for Python with pre-trained models for 53 languages.
The Python Foundation releases Python 3.7 on the Windows 10 App Store.
Amazon offers recommendations to policymakers on the use of facial recognition technology and calls for regulation of its use.
AWS open-sources the Neo-AI project, a machine learning compiler and runtime that tunes Tensorflow, PyTorch, ONNX, MXNet and XGBoost models for performance on edge devices.
Google introduces FEAST, an open-source "feature store" for managing and discovering features in machine learning models.
Google releases Natural Questions, a corpus of 300,000 questions and human-annotated answers, for question-answering research.
Uber open-sources Ludwig, a platform for training and testing deep learning models without programming, simply by specifying input and output variables in data.
Microsoft has joined the SciKit-learn Consortium as a Platinum member.
Azure Data Explorer, a high-performance service for real-time analysis of streaming data, is now generally available.
HDInsight Tools for VSCode, providing a lightweight code editor for HDInsight PySpark and Hive batch jobs, is now available.
Azure Cognitive Services can now be used with a unified API key, and in more regions and with new certifications including HIPAA BAA.
An in-depth InfoWorld review of Azure ML services.
Papers with Code, a repository of machine learning papers with associated implementation code and evaluation metrics.
Practical Deep Learning for Coders, the popular free course from fast.ai, has been updated with all-new content. Microsoft Azure and GCP provide pre-configured environments with the required tools, data and materials.
Recommenders Hub: an open source GitHub repository containing Microsoft’s best practices and state-of-the-art algorithms for building recommendation systems.
Tutorial: running trained deep learning models on Apple devices, by converting CoreML exported from PyTorch into ONNX.
Tutorials from the MIT Deep Learning courses: Deep learning basics, Driving scene segmentation, DeepTraffic deep reinforcement learning.
Reference Architecture: Batch scoring of Spark models on Azure Databricks.
Facebook develops a predictive model for global electricity availability, publishes the output and some code.
The British Broadcasting Corporation releases an R package and a guide for data journalists on creating data visualizations in the BBC News style.
A profile of Heather Dowdy, the head of Microsoft's AI for Accessibility program.
Find previous editions of the monthly AI roundup here.
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