Revolutions

AI, Machine Learning and Data Science Roundup: May 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

PyTorch 1.1 is now available, with new support for Tensorboard and improvements to distributed training and JIT compilation.

JupyterHub 1.0 is released, a milestone for the multi-user Jupyter Notebook server.

matplotlib 3.1 is released, adding several improvements to the Python data visualization library.

Python is now included in Windows 10, with updates available via the Microsoft Store.

FastBert, a simple PyTorch interface for training text classifiers based on the popular language representation model BERT, is released.

torchvision 0.3, the PyTorch library of datasets and tools for computer vision, adds new models for semantic segmentation and object detection.

ML.NET 1.0, the open-source cross-platform machine learning framework for .NET developers, is now available.

ONNX Runtime 0.4 is released, adding support for Intel and NVIDIA accelerators to further reduce latency for deployed neural networks.

R 3.6.0 is released, with many new capabilities and improved memory usage and performance.

Industry News

The Wolfram Engine, featured in Mathematica and including a suite of algorithms for visualization, machine learning, NLP and more, is now available free for development (license required for production).  

Facebook releases Pythia, a new open-source deep learning framework based on PyTorch, for multitasking in the vision and language domain.

Google Cloud AutoML Natural Language provides an interactive UI to classify content and build a predictive ML model without coding.

Google Cloud TPU Pods, cloud-based clusters that can include more than 1,000 TPU chips as an "ML supercomputer", are now publicly available in beta. NVIDIA T4 GPUs are also now generally available in GCP.

Snips open-sources Tract, an embedded neural network inference engine designed for wake-word detection by virtual assistant devices.

Hewlett Packard Enterprises announced plans to acquire Cray, the supercomputer company.

Intel announces several open-source initiatives for AI and cloud technologies, including a Deep Learning Reference Stack optimized for Intel chipsets.

Microsoft News

Azure Machine Learning Service adds new MLOps capabilities, providing version control, audit trails, and packaging, deployment and monitoring support for machine learning models via an Azure DevOps extension. Also, model deployment to FPGA is now generally available in Azure (and additionally in preview for Databox Edge).

Further updates to Azure Machine Learning Service are now in preview, including a new drag-and-drop visual interface, a new form-based UI for automated machine learning, model interpretability, and hosted Python notebooks.

Azure Cognitive Services has added many new capabilities, highlights of which include:

• A new category of service: Decision. The services include Content Moderator, Anomaly Detector and a new service called Personalizer (in preview).
• Conversation Transcription, a new Speech service that can discriminate and transcribe audio from multiple speakers in real time.
• Ink Recognizer, a new Vision service that allows developers to add digital handwriting recognition to apps.
• Additional Cognitive Services offering container support: Anomaly Detector, Speech-to-Text and Text-to-Speech.
• Azure Search, which includes many cognitive skills including image recognition and language comprehension, is now generally available.
• Form Recognizer, a new knowledge discovery service that extracts key-value pairs and tables from forms in scanned and PDF documents, now in preview.

Visual Studio Code adds Remote Development, allowing use of remote Python workspaces over SSH, in Docker containers, and in Windows Subsystem for Linux. Other recent Python support improvements include Intellisense in the console and enhancements to the Python Language Server.

Azure Data Explorer now supports queries with custom Python code, as well as integration with Spark.

Azure SQL Database Edge, a small-footprint data engine with support for Python, R and Spark and optimized for edge devices and time-series data, is now in private preview.

Microsoft announces an end-to-end toolchain for autonomous systems (in preview), which developers can use to simulate and build robots and other AI-driven autonomous devices.

Microsoft R Open 3.5.3 is released; new R packages AzureKusto (interface to Data Explorer) and AzureGraph (interface to the Microsoft Graph), are available.

Learning resources

A beginner's tutorial on training a convolutional neural network, using only Python and numpy.

Rules of Machine Learning: Google's list of best practices for developers looking to create applications with machine learning capabilities. 

ODSC suggests 25 public data sets to get started with machine learning, spanning text, images, and tabular data.

Foundations of Data Science, a free book by Avrim Blum, John Hopcroft and Ravi Kannan with a focus on matrix decompositions and associated ML techniques.

Azure Open Datasets, a collection of curated public datasets easily accessible to Azure ML services.

Open Images v5, a large collection of annotated images including segmentation masks for 2.8 million objects in 350 categories, has been released by Google.

Microsoft Learn now offers several modules with free training for AI engineers and data scientists.

Six principles behind health data-driven organizations, a useful resource on building a data science culture by Francesca Lazzeri.

Applications

How Python is used at Netflix for personalization, machine learning, experimentation, statistical analysis and more.

Google develops a method to infer depth maps for video by using "mannequin challenge" videos as training data.

Samsung researchers develop a method of animating a single photograph of a person as a realistic talking head (video).

Google's "Translatotron", a speech-to-speech translation model that translates speech audio into a second language while retaining the original speaking voice.

AI-based applications help children with disabilities bridge language gaps.

Facebook researchers develop a method to synthesize full-body video of a person performing actions animated in real time under joystick control, based only on real source video.

LaLiga and BMW are using the Azure Bot Framework SDK to deliver specialized personal assistant applications.

Find previous editions of the monthly AI roundup here.

AI, Machine Learning and Data Science Roundup: April 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

Facebook open-sources PyTorch-BigGraph for producing embeddings for graphs where the model is too large to fit in memory. Also published: an embeddings graph for 50 million Wikipedia concepts.

Visual Studio Code expands Python support, including a new variable explorer and data viewer, improved debugging capabilities, and real-time collaboration via Live Share.

Pyright, a static type-checker for Python, available as a command-line tool and a VS Code extension.

Frequently Asked Questions about Tensorflow 2.0, now in alpha testing.

VoTT, an open source annotation and labeling tool for image and video assets, has been updated to version 2.

Industry News

Google announces AI Platform (beta), an integrated platform for developers to create AI capabilities and deploy them to GCP or on premises.

New enterprise AI solutions from Google (in beta): Document Understanding AI, Contact Center AI, and Recommendations AI for retail.

Google made a number of other of AI-related announcements at Google Next, including new ML capabilities in BigQuery, AutoML Tables to generate an ML model predicting a data column with one click, updated pre-trained models for vision and natural language services, general availability of Cloud TPU v3, and more.

NVIDIA Tesla G4 GPUs are now available in Google Colab for faster training with larger models.

Microsoft News

Microsoft commits to host the world's leading environmental data sets on Azure as part of a broader sustainability initiative.

Microsoft's research in Machine Teaching: the process of augmenting models with human input rather than using data alone.

Anomaly Detector, a new Azure Cognitive Service to detect unusual values in time series data, is now in preview.

Azure Custom Vision, the transfer learning service for image classification with user-provided labels, is now generally available.

Azure Video Indexer can now be trained to recognize specific people in video from user-provided photographs.

Azure Cognitive Services are now accessible in Apache Spark workloads, via Spark ML pipelines.

You can now deploy Tensorflow models to Azure Data Box Edge with Azure ML Service and ONNX, for local inference on high-performance FPGA chips.

Azure HDInsight now supports Apache Hadoop 3.0, and updated versions of Hive, HBase, and Phoenix.

Managed MLflow is now generally available in Azure Databricks, to track experiments and manage models and projects. Microsoft has also joined the open-source MLflow project.

PowerBI now offers AutoML, to enable business analysts to build machine learning models from data without coding.

Learning resources

Advanced Natural Language Processing with spaCy, a free online course from Ines Montani, a core developer of the Python package. These tutorial notebooks on sentiment analysis by Ben Trevett also make use of spaCy.

The Economist reworks some of their worst published charts, providing a lesson in better data visualization.

Discriminating Systems: Gender, Race and Power in AI: Results from a year-long study on diversity in the AI sector from the AI Now Institute.

A tutorial on creating a serverless HTTP endpoint with Python and Azure Functions using Visual Studio Code.

Tutorial: incrporating predictions from an R model in Power BI report.

AWS Machine Learning University: free access to the curriculum used to teach Amazon staff about ML on AWS.

An introduction to reinforcement learning, with AWS RoboMaker.

Applications

Ganvatar: an impressive demonstration of synthesizing faces along semantic vector axes (age, gender, happiness).

Using the time series and outlier detection features of the Kusto Query Language to detect cyber threats with Azure Sentinel.

Forecasting orange juice sales in a grocery chain (Jupyter Notebook), using automated machine learning in Azure ML Service.

Seek, a smartphone app that uses computer vision to identify plant and animal species in real time.

Find previous editions of the monthly AI roundup here.

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.

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.

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.

Because it's Friday: Synthetic faces, styled to your specifications

If you need someone's face to use in an application or some marketing materials, you might search one of the stock photography vendors for people of a given gender, skin tone, hairstyle, etc. Or, you could just ask the Generative Adversarial Network described in this paper by NVIDIA researchers to generate a brand new face for you. See it in action in the video below:

That's all for this week. Have a great weekend, and we'll be back with more from the blog next week.

Notebooks from the Practical AI Workshop

Last month, I delivered the one-day workshop Practical AI for the Working Software Engineer at the Artificial Intelligence Live conference in Orlando. As the title suggests, the workshop was aimed at developers, bu I didn't assume any particular programming language background. In addition to the lecture slides, the workshop was delivered as a series of Jupyter notebooks. I ran them using Azure Notebooks (which meant the participants had nothing to install and very little to set up), but you can run them in any Jupyter environment you like, as long as it has access to R and Python. You can download the notebooks and slides from this Github repository (and feedback is welcome there, too). 

The workshop was divided into five sections, each with its associated Notebook:

1. The AI behind Seeing AI. Use the web interfaces to Cognitive Services to learn about the AI services behind the "Seeing AI" app
2. Computer Vision API with R. Use an R script to interact with the Computer Vision API and generate captions for random Wikimedia images.
3. Custom Vision with R. An R function to classify an image as a "Hot Dog" or "Not Hot Dog", using the Custom Vision service.
4. MNIST with scikit-learn. Use sckikit-learn to build a digit recognizer for the MNIST data using a regression model.
5. MNIST with Tensorflow. Use Tensorflow (from Python) to build a digit recognizer for the MNIST data using a convolutional neural network.

The workshop was a practical version of a talk I also gave at AI Live, "Getting Started with Deep Learning", and I've embedded those slides below. 

Azure Notebooks: Practical AI for the Working Software Engineer

AI, Machine Learning and Data Science Roundup: December 2018

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

PyTorch 1.0 has been released with faster performance, better support for distributed computing, a new C++ frontend, and a model-sharing repository.

Tensorflow 2.0 will include a built-in implementation of Keras for deeper integration and support of eager execution.

Industry News

The AI Now Institute has published the AI Now Report 2018, and makes 10 recommendations related to the social impact of AI technology.

Stanford's AI Index 2018 Annual Report documents the year's progress in AI teaching, research, and humanlike capabilities.

Tableau adds SSL support for connecting the R server for its R integration feature.

AWS Lambda adds support for Python 3.7, and offers a customization framework for supporting other programming languages.

New features in Amazon SageMaker: semantic segmentation, early stopping for automatic model tuning, and tools for tracking machine learning experiments.

H20.ai's machine learning platform now available as a hosted service on AWS.

Microsoft News

Microsoft calls for government regulation on the use of facial recognition technology.

The Microsoft Connect event on December 4 included several product announcements related to Machine Learning and AI, summarized below.

Azure Machine Learning service is now generally available. This service orchestrates the training and deployment of machine learning modules, using resources managed on-premises or in Azure. Capabilities include:

• Model training with open-source frameworks including scikit-learn, PyTorch and Tensorflow.
• A Python module for managing and orchestrating models and compute resources.
• Integration with developer tools including Jupyter Notebooks and Visual Studio Code.
• The ability to launch and redirect training to CPU and GPU-enabled resources: local, Azure virtual machines, and distributed clusters with auto-scaling capabilities.
• An AI-driven service for testing and optimizing multiple ML algorithms, and hyperparameter tuning for PyTorch and Tensorflow models.
• Production deployment for trained models via containers and the open ONNX standard.
• Pipelines to orchestrate the entire machine learning process from data to deployment.

Azure Notebooks, the hosted Jupyter Notebooks service, has been updated. New features include:

• A refreshed user interface, with improved project management and notifications.
• Upgrades to the free compute image, and the ability to use paid virtual machine (including GPU-class) instances to provide the compute kernel.
• Streamlined integration with Azure Authentication and Azure Machine Learning service.

ONNX Runtime, a cross-platform, high-performance engine for inferencing with trained ML models in the Open Neural Network Exchange (ONNX) representation, has been released as open source.

ML.NET 0.8, the open-source machine learning framework for .NET, is now available with new recommendation scenarios and a feature importance tool.

The Language Understanding service can now be deployed in a self-hosted container.

The Translator Text API now offers customized translation to support specialized expressions and vocabulary.

Neural Text to Speech, a preview feature of Cognitive Services Speech Service, synthesizes spoken voices indistinguishable from real people.

Azure Functions now supports serverless deployment of Python functions. This blog post provides detailed examples.

Azure SQL Database now provides integration with the R language (in preview).

AzureR: a new suite of packages to manage Azure services with R.

Learning resources

The book Bayesian Methods for Hackers is now available as a series of Jupyter Notebooks with code in Tensorflow.

A tutorial on causal inference in computing systems, presented by Amit Sharma and Emre Kicima at KDD 2018.

A guide to resources and tutorials for getting started with machine learning on Azure.

A tutorial on packaging Cognitive Services APIs into containers and running them on a Docker-enabled platform.

Pattern Recognition and Machine Learning, the 2006 book by Christopher M Bishop, is now available for free download.

A 4-part series on the history and recent advancements in neural word embeddings.

Applications

Using a computer vision model to measure building footprints in satellite images.

Using active learning to track the migration of endangered bird species.

Using deep learning to improve the effectiveness of diabetic retinopathy models.

Using computer vision to quantify fish stocks in Australia.

A collection of reference architectures for deploying machine learning and AI models to real-time applications and batch processes with Azure.

Find previous editions of the monthly AI roundup here.

AI, Machine Learning and Data Science Roundup: November 2018

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

torchdiffeq: A GPU-enabled PyTorch library for ordinary differential equation (ODE) solving.

Tensorflow 1.12 released.

Visual Studio Code adds support for Jupyter Notebooks, with the ability to import Notebooks into Code, and interact with cells via the iPython kernel.

Industry News

Amazon Comprehend, the AWS natural language processing service, can now be customized with user-defined entities and categories.

Google Cloud Platform introduces AI Hub, a place to publicly host and privately (within-enterprise) share and deploy pipelines, Jupyter notebooks, TensorFlow modules, and more.

A Chrome extension to open a Github-hosted Jupyter Notebook in Google Colab.

Facebook open-sources Horizon, a reinforcement learning platform that exports models in ONNX for deployment in large-scale production environments.

Google open-sources BERT (Bidirectional Encoder Representations), a system for pre-training natural language models with large corpuses of unannotated text.

Microsoft News

Microsoft has acquired XOXOCO, a startup focused on chatbots.

Microsoft Cognitive Services in Containers. Face detection and text analytics APIs in self-hosted containers that do not require private data to be sent to the cloud for analysis.

Named Entity Recognition comes to the Cognitive Services Text Analytics API, to identify persons, locations, organizations and other entities in unstructured text.

Azure Machine Learning Studio upgrades R support with the addition of the R 3.4 language engine.

New AI capabilities in preview for Power BI: AI model builder, Cognitive Services integration, and Azure Machine Learning interface. 

The Microsoft Data Science Virtual Machine now includes Catboost, the open-source gradient boosting on decision trees library.

AzureR, a new suite of packages for managing Azure services from R.

Learning resources

A guide to translate between different terms used for similar concepts in Statistics and Machine Learning, from CMU.

Best Practices for Using Machine Learning in Businesses: Szilard Pafka cuts through the hype in this Twitter-summary of his keynote presentation.

A comparison of automatic machine learning services: Google AutoML, Microsoft Automated ML, AutoKeras and auto-sklearn.

Using Docker to deploy an R API with the plumber package.

Applications

Google uses acoustic analysis to identify humpback whales by their call.

Uber's approach to evaluating experiments: look at the entire distribution of outcomes, because focusing on just the mean or median can be misleading.

T-mobile uses R in production to route customer service queries with natural language AI.

Find previous editions of the monthly AI roundup here.

Cognitive Services in Containers

I've posted several examples here of using Azure Cognitive Services for data science applications. You can upload an an image or video to the service and extract information about faces and emotions, generate a caption describing a scene from a provided photo, or speak written text in a natural voice. (If you haven't tried the Cognitive Services tools yet, you can try them out using the instructions in this notebook using only a browser.)

But what if you can't upload an image or text to the cloud? Sending data outside your network might be subject to regulatory or privacy policies. And if you could analyze the images or text locally, your application could benefit from reduced latency and bandwidth. 

Now, several of the Azure Cognitive Services APIs are available as Docker containers: you can download a container that provides the exact same APIs as the cloud-based services, and run it on a local Linux-based server or edge device. Images and text are processed directly in the container and never sent to the cloud. A connection to Azure is required only for billing, which is at the same rate as the cloud-based services (including a free tier).

The services now available as containers are:

• Face: face detection, identity verification, and emotion detection. In private preview: sign up here. Free and paid tier details here.
• Recognize text: detect and extract printed text from images. In private preview: sign up here. Free and paid tier details here.
• Text Analytics: Key phrase extraction, language detection, and sentiment analysis. In public preview, available to all Azure subscribers. Free and paid tier details here.

You can learn more about Cognitive Services in the video below. (The information about the new container support starts at 11:20.)

You can also find detailed information about the Cognitive Services in containers in the blog post linked below.

Microsoft Azure blog: Getting started with Azure Cognitive Services in containers