Announcing Cortana Intelligence Solution Evaluation Tool

This post is authored by Avi Bathula, Sr. Program Manager Lead, and Jamie Olson, Senior SDE on the Microsoft Cloud & AI Ecosystem team. Most of you know about Microsoft’s Cortana Intelligence and how solutions/apps built with Cortana Intelligence are already helping organizations transform their data into actionable insights. As noted in the linked blog… Read more

Using a Recommendation System in an Application

This post is authored by Ankit Asthana, Principal PM VS.NET at Microsoft. Recommendation systems are extremely popular today and are used everywhere, to predict music you’d like, products to buy, and movies to see! In this post, we would like to show you how you can build a movie recommendation engine. The post will describe… Read more

Using Azure Data Lake and R for Fraud Detection

This post is authored by Yiwen Sun, Data Scientist, Esin Saka, Senior Software Engineer, and Shravan Matthur Narayanamurthy, Senior Software Engineering Manager, at Microsoft. As petabyte-scale data becomes available, how to successfully enable and scale the traditional machine learning paradigm onto big data infrastructure is no longer a trivial matter for the likes of AI… Read more

Saving Snow Leopards with Deep Learning and Computer Vision on Spark

This post is authored by Mark Hamilton, Software Engineer at Microsoft, Rhetick Sengupta, President of Snow Leopard Trust, and Principal Program Manager at Microsoft, and Roope Astala, Senior Program Manager at Microsoft. The Snow Leopard – A Highly Endangered Animal Snow leopards are highly endangered animals that inhabit high-altitude steppes and mountainous terrain in Asia… Read more

Machine Learning for Developers – How to Build Intelligent Apps & Services

This post is authored by Daniel Grecoe, Senior Software Engineer at Microsoft. There’s a lot of talk about machine learning these days and how it will transform applications and services. Most of it is right on target: ML is definitely changing the way certain computing tasks will be implemented in the future, why wouldn’t it?… Read more

Data Virtualization: Unlocking Data for AI and Machine Learning

This post is authored by Robert Alexander, Senior Software Engineer at Microsoft. For reliability, accuracy and performance, both AI and machine learning heavily rely on large sets. Because the larger the pool of data, the better you can train the models. That’s why it’s critical for big data platforms to efficiently work with different data… Read more

Running BigDL Apache Spark Deep Learning Library on Microsoft Data Science Virtual Machine

This post is co-authored by Gopi Kumar at Microsoft and Sergey Ermolin at Intel. Introduction BigDL is a distributed deep learning library for Apache Spark. It has both Python and Scala interfaces and takes advantage of Spark-enabled distributed compute infrastructure, allowing users to write Deep Learning applications in a familiar native Spark context format. The Microsoft… Read more

Loading a Trained Model Dynamically in an Azure ML Web Service

This post is authored by Ahmet Gyger, Program Manager at Microsoft. We are very excited to announce the availability of a new module named “Load Trained Model”. As its name indicates, this module allows you to load a trained model (also referred to as an iLearner file) in Azure ML into an experiment at runtime…. Read more

Data Science Walkthrough with SQL Server 2017 and Microsoft Machine Learning Services

This post is authored by Xibin Gao, Wei Guo, and Debraj GuhaThakurta at Microsoft. Microsoft Machine Learning Services were a key highlight of our SQL Server 2017 CTP 2.0 release in April this year. It allows Python scripts to run within SQL Server or be embedded in SQL scripts and be deployed as stored procedures. This… Read more

Announcing Microsoft Machine Learning Library for Apache Spark

This post is authored by Roope Astala, Senior Program Manager, and Sudarshan Raghunathan, Principal Software Engineering Manager, at Microsoft. We’re excited to announce the Microsoft Machine Learning library for Apache Spark – a library designed to make data scientists more productive on Spark, increase the rate of experimentation, and leverage cutting-edge machine learning techniques –… Read more