Microsoft for Startups shares this highlight reel from the Spring MLADS conference.

In case you’re not familiar with MLADS, check out Data Driven’s coverage of the most recent one.

Twice a year, Microsoft assembles over 4,000 of our top data scientists and engineers for a two day internal conference to explore the state of the art around machine learning and data science.

Earlier this year, 30 leading startups who are active in the Microsoft for Startups program came to showcase their solutions and engage directly with the engineering teams.

Learn all about the new data classification capabilities built into Azure SQL Database. Data Classification enables discovering, classifying, labeling & protecting the sensitive data in your databases.

Examples of sensitive data include business, financial, healthcare, personally identifiable data (PII). Discovering and classifying your most sensitive data can play a pivotal role in your organizational information protection stature.

Data discovery & classification is part of the Advanced Data Security (ADS) offering, which is a unified package for advanced SQL security capabilities.

Find out more about Advanced Data Security at: https://docs.microsoft.com/en-us/azure/sql-database/sql-database-advanced-data-security?WT.mc_id=dataexposed-c9-niner-fw .

Microsoft Research has posted this interesting video:

To develop an Artificial Intelligence (AI) system that can understand the world around us, it needs to be able to interpret and reason about the world we see and the language we speak. In recent years, there has been a lot of attention to research at the intersection of vision, temporal reasoning, and language.

One of the major challenges is how to ensure proper grounding and perform reasoning across multiple modalities given the heterogeneity resides in the data when there is no or weak supervision of the data.

Talk slides: https://www.microsoft.com/en-us/research/uploads/prod/2019/11/Towards-Grounded-Spatio-Temporal-Reasoning-SLIDES.pdf

This video provides an overview of administration experiences for BDC (Big Data Clusters).

In big data clusters, we ensure that management services embedded with the platform provide fast scale and upgrade operations, automatic logs and metrics collection, enterprise grade secure access and high availability.

If you have large data sets that seem too big to map, then watch this IoT show to learn how to cluster data in Azure Maps so your users can rapidly extract insights from very large data sets.

Ricky Brundritt, Principal Technical Program Manager, Azure Maps, takes you on a historical journey from grid-based clustering to radius-based clustering. You’ll learn how the power of the open source community has contributed to the supercluster library which Azure Maps leverages extensively. Watch Ricky demo and step through Azure Maps code for clustering using large data sets of shipwrecks and earthquakes. Learn how to use cluster aggregates to perform calculations based on properties of the children of each cluster. Ricky wraps up with a demo visualizing clustered map data in the form of pie charts—again, to enable your users to extract insights quickly.

Access demo source code here: https://aka.ms/AzureMapsSamples