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March 21, 2019

AI, Machine Learning and Data Science Roundup: March 2019

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

TensorFlow Privacy: a Python library for training machine learning models with differential privacy, for use with sensitive data to generate models that don't learn details about specific people.

Tensorflow Federated, an open-source library for Federated Learning, enabling many participating clients to train shared ML models while keeping their data local.

R 3.5.3 has been released.

NNI, an open source AutoML toolkit for neural architecture search and hyper-parameter tuning, from Microsoft Research.

Industry News

Open AI has published a paper describing GPT-2, an unsupervised language model that can generate paragraphs of coherent text that could be mistaken for human writing. Only a scaled-down version has been released, for fear of abuse.

Mozilla releases Common Voices, a public domain dataset of 1,400 hours of recorded voice data in 18 languages.

GCP's Deep Learning Virtual Machine now incorporates RAPIDS, the open-source GPU-acceleration library for Python machine learning.

Google introduces GPipe, an open-source library for training large-scale deep neural networks on devices with otherwise insufficient resources.

Determined AI, platform-agnostic software for automated machine learning and infrastructure management, has been announced.

Microsoft News

Azure Machine Learning Service now incorporates RAPIDS, the open source NVIDIA library that provides accelerated GPU support for Pandas dataframes and scikit-learn machine learning algorithms.

ONNX Runtime adds the NVIDIA TensorRT execution provider, for improved inferencing support on NVIDIA GPU devices.

The Intel Optimized Data Science Data Science Virtual Machine, providing up to 10x performance increase for CPU-based deep learning workloads, is now available on Azure Marketplace.

New Python features in VS Code include validation of breakpoint targets, a new test explorer, and the ability to run selected code without the need to define code cells.

New enhancements for Cognitive Services Computer Vision: bounding boxes for detected objects, and more types of objects detected (including thousands of brand logos).

Azure Cognitive Services containers for Text Analytics and Language Understanding containers are now supported on edge devices with Azure IoT Edge.

MMLSpark 0.16 is released, with improvements to machine learning methods for Apache Spark including Azure Search integration and the Smart Adaptive Recommender.

Microsoft Research releases MT-DNN as a PyTorch package, an architecture for natural language understandings that combines elements of MTL and BERT.

Learning resources

Microsoft launches AI Business School, a collection of learning modules aimed at business leaders on AI strategy, technology and responsible impact.

A high-level article on how and why transfer learning works, and the connection with embeddings.

The Quartz AI Studio, a task-oriented resource for data journalists, provides useful guides for text and image analysis for general practitioners as well.

Causal Inference: The Mixtape, a freely-licensed book by Scott Cunningham that comes with its own playlist.

An architecture for using R at scale on GCP, by Mark Edmondson.

The Python Data Science Handbook (by Jake VanderPlas) in Jupyter Notebook form. Also available in Azure Notebooks and Google Colab.

Datasets for Machine Learning: A list of the biggest datasets from across the web.

Applications

SPADE, a method for synthesizing photo-realistic images from a simple sketch.

Seeing AI now includes a feature to help vision-impaired users explore photographs by touch, and is now supported on iPad devices.

Google deploys a compact RNN transducer to mobile phones that can transcribe speech on-device and streams output letter-by-letter, and a quasi-recurrent neural network for handwriting transcription.

NVIDIA introduces the Kaldi ASR Framework for high-speed speech transcription.

Find previous editions of the monthly AI roundup here.

Posted by at 08:27 in AI, airoundups | | Comments (3)

March 12, 2019

R 3.5.3 now available

The R Core Team announced yesterday the release of R 3.5.3, and updated binaries for Windows and Linux are now available (with Mac sure to follow soon). This update fixes three minor bugs (to the functions writeLines, setClassUnion, and stopifnot), but you might want to upgrade just to avoid the "package built under R 3.5.4" warnings you might get for new CRAN packages in the future.

R releases typically reference the Peanuts cartoon, but the code-name for this release, "Great Truth", is somewhat of a mystery. There may be a clue in the release date, March 11. Anyone got any ideas?

For more details on this latest update to the R language, check out the announcement below. And as always, thanks to the members of the R Core Team for their contributions to all R users with the R project.

R-announce mailing list: R 3.5.3 is released

 

Posted by at 16:45 in announcements, R | | Comments (2)

March 07, 2019

Where to find the worst weather in the US

Which US city has the worst weather? To answer that question, data analyst Taras Kaduk counted the number of pleasant days in each city and ranked them accordingly. For this analysis, a "pleasant" day is one where the average temperature was in the range 55°F-75°F, the maximum was in the range 60°-90°, the minimum was in the range 40°-70°, and there was no significant rain or snow. (The weather data was provided by the NOAA Global Surface Summary of the Day dataset and downloaded to R with the rnoaa package.)

With those criteria and focusing just on the metro regions with more than 1 million people, the cities with the fewest pleasant days are:

  1. San Juan / Carolina / Caguas, Puerto Rico (hot year-round)
  2. Rochester, NY (cold in the winter, rain in the summer)
  3. Detroit / Warren / Dearborn, MI (cold in the winter, rain in the summer)

You can see the top (bottom?) 25 cities in this list in the elegant chart below (also by Tara Kaduk), which shows each city as a polar bar chart and with one ring for each of the 6 years of data analyzed. 

25_least_1000_polar_

And if you're wondering which cities have the best weather, here's the corresponding chart for the 25 cities with the most pleasant days. San Diego / Carlsbad (CA) tops that list.

25_most_1000_polar_

You can find the R code behind the analysis and charts in this Github repository. (The polar charts above required a surprisingly small amount of code: it's a simple transformation of a regular bar chart with the ggplot2 coord_polar transformation — quite appropriate given the annual cycle of weather data.) And for the full description of the analysis including some other nice graphical representations of the data, check out the blog post linked below.

Taras Kaduk: Where are the places with the best (and the worst) weather in the United States

Posted by at 12:20 in government, graphics, R | | Comments (5)

March 01, 2019

An architecture for real-time scoring with R

Let's say you've developed a predictive model in R, and you want to embed predictions (scores) from that model into another application (like a mobile or Web app, or some automated service). If you expect a heavy load of requests, R running on a single server isn't going to cut it: you'll need some kind of distributed architecture with enough servers to handle the volume of requests in real time.

This reference architecture for real-time scoring with R, published in Microsoft Docs, describes a Kubernetes-based system to distribute the load to R sessions running in containers. This diagram from the article provides a good overview:

Realtime-scoring-r-architecture

You can find detailed instructions for deploying this architecture in Github. This architecture uses Azure-specific components, but you could also use their open source equivalents if you wanted to host them yourself:

For more details on this architecture, take a look at the Microsoft Docs article linked below.

Microsoft Docs: Real-time scoring of R machine learning models

Posted by at 11:04 in developer tips, Microsoft, R | | Comments (1)

February 21, 2019

Developments in AzureR

by Hong Ooi, senior data scientist, Microsoft Azure

The AzureR packages have now been on CRAN for a couple of months, so I thought I'd provide an update on developments in the works.

First, AAD authentication has been moved into a new package, AzureAuth, so that people who just want OAuth tokens can get it without any other baggage. This has many new features:

  • Supports both AAD v1.0 and v2.0
  • Tokens are cached in a user-specific directory using the rappdirs package, typically c:\users\<username>\local\AzureR on Windows and ~/.local/share/AzureR on Linux
  • Supports 4 authentication methods: authorization_code, device_code, client_credentials and resource_grant
  • Supports logging in with a username or with a certificate

In the longer term, the hope is for AzureAuth to be something like the R equivalent of the ADAL client libraries. Things to add include dSTS, federated logins, and more.

AzureRMR 2.0 has a new login framework and no longer requires you to create a service principal (although you can still provide your own SP if desired). Running create_azure_login() logs you into Azure interactively, and caches your credentials; in subsequent sessions, run get_azure_login() to login without having to reauthenticate.

AzureStor 2.0 has several new features mostly for more efficient uploading and downloading:

  • Parallel file transfers, using a pool of background processes. This greatly improves the transfer speed when working with lots of small files.
  • Transfer files to or from a local connection. This lets you transfer in-memory R objects without having to create a temporary file first.
  • Experimental interface to AzCopy version 10. This lets you do essentially anything that AzCopy can do, from within R. (Note: AzCopy 10 is quite a different beast to AzCopy 8.x.)
  • A new framework of generic methods, to organise all the various storage-type-specific functions.

A new AzureKusto package is in the works, for working with Kusto/Azure Data Explorer. This includes:

  • All basic functionality including querying, engine management commands, and ingesting
  • A dplyr interface written by Alex Kyllo.

AzureStor 2.0 is now on CRAN, and the others should also be there over the next few weeks. As always, if you run into any problems using the packages, feel free to contact me.

Posted by at 08:30 in Microsoft, packages, R | | Comments (1)

February 14, 2019

AI, Machine Learning and Data Science Roundup: February 2019

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.

PT-BERT, a PyTorch implementation of Google's BERT language representation model, adds new pre-trained language models: GPT and Transformer-XL.

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.

Industry News

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 News

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.

Learning resources

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.

Presentation videos and workshop materials from the recent RStudio conference are available.

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.

Applications

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.

TWIMLAI's podcast series on Microsoft's AI for Earth , AI for Accessibility and AI for Humanitarian Action programs.

A profile of Heather Dowdy, the head of Microsoft's AI for Accessibility program.

Find previous editions of the monthly AI roundup here.

Posted by at 10:41 in AI, airoundups, Microsoft, python, R | | Comments (3)

February 07, 2019

Create data visualizations like BBC News with the BBC's R Cookbook

If you're looking for a guide to making publication-ready data visualizations in R, check out the BBC Visual and Data Journalism cookbook for R graphics. Announced in a BBC blog post this week, it provides scripts for making line charts, bar charts, and other visualizations like those below used in the BBC's data journalism. 

BBC-graphics

The cookbook is based around the bbplot package (available on Github), which has been in use at the BBC since March 2018 for creating graphics in the BBC News graphics style. Rather than drafting graphics in R and then relying another graphics tool or a design team for publication, R has been a "game changer" for the BBC. It offers more creativity and control while saving time and effort, especially when the chart needs to be reproduced every time the underlying data change. The BBC offers a six-week course for its data journalists internally, and you can learn as well with the free cookbook published by the BBC, linked below.

Github (BBC): BBC Visual and Data Journalism cookbook for R graphics

 

 

Posted by at 13:58 in applications, graphics, R | | Comments (2)

February 01, 2019

Tutorial: Sequential Pattern Mining in R for Business Recommendations

by Allison Koenecke, Data Scientist, AI & Research Group at Microsoft, with acknowledgements to Amita Gajewar and John-Mark Agosta.

In this tutorial, Allison Koenecke demonstrates how Microsoft could recommend to customers the next set of services they should acquire as they expand their use of the Azure Cloud, by using a temporal extension to conventional Market Basket Analysis. 

Problem Statement

Market Basket Analysis (MBA) answers a standard business question: given a set of grocery store receipts, can we find bundles of products often purchased together (e.g., peanut butter and jelly)?  Suppose we instead want to model the evolution of a customer’s services, such as determining whether buying peanut butter in the past indicates a higher likelihood of buying bread in the future.  For this, we apply a sequential version of MBA, sometimes called “sequential itemset mining” or “sequential pattern mining”, to introduce a time component to the analysis [1]. Sequential itemset mining has been applied across many industries, from determining a patient’s sequence of medical prescriptions [2] to detecting misuse intrusions such as application layer attacks [3]. In this tutorial, when given a series of purchases over time, we determine whether we can (1) find product bundles that we expect to be consumed simultaneously, and also (2) examine how these bundles evolve over time.

In the following tutorial, we answer both questions using the R package arulesSequences [4], which implements the SPADE algorithm [5]. Concretely, given data in an Excel spreadsheet containing historical customer service purchase data, we produce two separate Excel sheet deliverables: a list of service bundles, and a set of temporal rules showing how service bundles evolve over time.  We will focus on interpreting the latter result by showing how to use temporal rules in making predictive sales recommendations.

Our running example below is inspired by the need for Microsoft’s Azure Services salespeople to suggest which additional products to recommend to customers, given the customers’ current cloud product consumption services mix.  We’d like to know, for instance, if customers who have implemented web services also purchase web analytics within the next month.  Actual Azure Service names have been removed for confidentiality reasons.

Step 1: Reformat Data into Transaction Matrix

Continue reading "Tutorial: Sequential Pattern Mining in R for Business Recommendations" »

Posted by at 10:30 in Microsoft, R, statistics | | Comments (4)

January 22, 2019

RStudio Server on Azure

RStudio Server Pro is now available on the Azure Marketplace, the company announced on the RStudio Blog earlier this month. This means you can launch RStudio Server Pro on an virtual machine with the memory, disk, and CPU configuration of your choice, and pay by the minute for the VM instance plus a the RStudio software charge. Then, you can use a browser to access the remote RStudio Server (the interface is nigh-indistinguishable from the desktop version), with access to the commercial features of RStudio including support for multiple R version and concurrent R sessions, load-balancing and high availability instances, and enhanced security.

If you don't want or need those capabilities, you can use the open-source version of RStudio Server as well. The easiest way is simply to launch an instance of the Azure Data Science Virtual Machine, which comes pre-installed with the open-source RStudio Server. It also comes pre-installed with dozens of data science tools you can use with R and RStudio, including Jupyter Notebooks, Python (check out the reticulate package), Keras and Tensorflow, and much more. All of the tools are free and/or open-source, so you only pay the base virtual machine charge (which depends on your region and the size and power of the instance you select). Once you've launched the Ubuntu DSVM, simply log in and use systemctl to start the RStudio Server service, and then use your browser to access RStudio remotely.

DSVM

If you don't need a server with all the tools the DSVM provides, you can configure your own VM with just RStudio Server and anything else you need, too. This handy blog post by Scott James Bell takes you through the steps. This includes setting up an Azure account (if you don't have one yet, click here for $200 in Azure Credits to get started), creating a virtual machine instance, and installing R, RStudio, and Shiny Server (for hosting Shiny applications) on it. Follow the link below for the complete details, and enjoy using RStudio on Azure!

My Year in Data: Running a Shiny Server on Microsoft Azure

Posted by at 15:09 in Microsoft, R | | Comments (0)

January 16, 2019

AI, Machine Learning and Data Science Roundup: January 2019

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

Preview of Tensorflow 2.0 (the public preview is expected "early this year").

pcLasso, an R package implementing a new method for supervised learning described by co-author Rob Tibshirani as "principal components regression meets the lasso".

R 3.5.2 has been released.

Industry News

A retrospective of Google Research activities in AI in 2018.

Google Cloud Platform now supports R jobs (via SparkR) in Cloud DataProc.

GCP App Engine support for Python 3.7 now generally available.

Amazon SageMaker now comes preconfigured with support for SciKit-learn.

Microsoft News

Microsoft Professional Program for Data Analysis, a new online course and certification. Other MPP tracks include Data Science and Artificial Intelligence.

[video] A single API key can now be used to access Cognitive Services APIs for language, vision and search.

AzureR, a suite of packages for interfacing with storage, virtual machines, containers and other Azure services from the R language, is now available on CRAN.

Learning resources

E-book by TWIML AI host Sam Charrington: Kubernetes for Machine Learning, Deep Learning and AI (requires free sign-up).

E-book by Patrick Hall and Navdeep Gill: Introduction to Machine Learning Interpretability (requires free registration).

An in-depth introduction to convolutional neural networks, from Ars Technica: How computers got shockingly good at recognizing images.

An on-line course from Databricks, Deep Learning Fundamental Series, with a focus on Keras and TensorFlow.

Getting Started with TensorFlow Probability, from R, a blog post from RStudio.

What can Neural Networks Learn?, an approachable look at the inner workings of neural network classifiers from Brandon Rohrer.

A blog post describing the computational graph concepts behind TensorFlow, including an illustrative implementation of core TensorFlow operations in numpy.

Applications

Using computer vision to monitor shelf stocking policies, a three-part series on complex image classification: Part 1, Part 2, Part 3.

[video] Building a pet breed identification application via transfer learning with Azure ML Service and Python. (Github repo here.)

Find previous editions of the monthly AI roundup here.

Posted by at 14:50 in AI, airoundups, python, R | | Comments (1)

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