Microsoft Machine Learning & Data Science Conference

The spring 2015 edition of the Machine Learning & Data Science (MLDS) Conference for Microsoft employees concluded two weeks ago. This conference, which is held twice yearly, is a key part of the vibrant community around ML and data science within Microsoft. In it, we cover the entire spectrum of Advanced Analytics (AA), from data discovery and ML algorithms to the end-to-end data science process and real-world customer apps.

These technologies are increasingly used in a new generation of intelligent apps that are bringing about unprecedented insights into our ever-expanding universe of digital data.

The event saw tremendous interest with thousands of attendees, both in-person and employees joining online around the world. The conference featured over 65 talks, 60 posters/demos, and 15 tutorials, each of which shone a bright light on cutting-edge work happening in different parts of our company. Here’s a short video to give you a flavor of this year’s spring event:

The opening Keynote was by R. Preston McAfee, Corporate Vice President (CVP) and Chief Economist at Microsoft. Preston talked about some of the key considerations around advertising exchanges and auctions, these being among the biggest ML systems ever built, and certainly among the most revenue-generating.

Conference Talks spanned a variety of topics, including Fundamentals; Data Discovery, Connectivity & Preparation; Algorithms; Languages & Tools; Data Science; Applications; IoT, Streaming & Time Series; Analytics on Large Datasets; Forecasting; Attacks, Intrusion & Malware; Vision; Customer Churn; Power BI & Data Visualization, and Search, Query Intent & Text Analytics.

Tutorials offered an opportunity for in-depth hands-on learning in areas such as Azure Machine Learning, Python, R, Revolution R Enterprise (RRE), Big Data Analytics, Deep Neural Networks, A/B Testing, and many more.

The Poster & Demo Reception attracted over 600 attendees in a tradeshow-like format featuring over 60 projects spanning the whole gamut from Recommending a Recommender: An Algorithm Sommelier to Ranking in a Mobile-First World to Energy Demand Forecasting. 

At his Day 2 keynote, CVP Joseph Sirosh described how the cloud is eating both software and data, creating a huge opportunity for developers to build the next generation of intelligent apps and experiences that use the power of AA and the cloud. He shared four patterns that developers working in the cloud go after – Retrospective Analytics, Real-time Analytics, Predictive Analytics and Intelligent SaaS applications – along with stories and demos to illustrate these concepts, such as the viral How-Old.net site.

Attendees could get personalized assistance on any AA challenges that they faced from Microsoft experts on Azure ML, Revolution R Enterprise, Azure Data Factory, Power BI and more.

The conference concluded with the Panel Discussion Language, Text, Vision & NUI: When Is ML Useful & When Not? which covered wide-ranging issues such as whether machines can (or ought to) learn just like humans and ethical implications of artificial intelligence – including machines potentially making humans irrelevant, or machines teaching other machines.

Between the two annual MLDS conferences, the Microsoft AA community gathers regularly for smaller in-person and online events including hackathons and internal forums where practitioners share their expertise. The community also has access to an online Machine Learning University, which pulls together course materials on the most-requested topics in this area.

The goal for MLDS and other community activities is to help internal practitioners from around the world learn, share know-how and make Microsoft products and services even better through the power of Advanced Analytics.

ML Blog Team