Today, we are announcing the support for disaster recovery of virtual machines deployed in Availability Zones to another region using Azure Site Recovery (ASR). You can now replicate and failover zone pinned virtual machines to other regions within a geographic cluster using Azure Site Recovery. This new capability is generally available in all regions supporting Availability Zones. Along with Availability Sets and Availability Zones, Azure Site Recovery completes the resiliency continuum for applications running on Azure Virtual Machines.
Today, we are very pleased to announce significant updates to the ADLS Gen2 preview that will allow an even greater experience for customers. These updates include integration with Databricks and HDI, Azure Storage Explorer support and more.
We’re excited to share the general availability of Virtual Network (VNet) Service Endpoints for Azure SQL Data Warehouse in all Azure regions. Azure SQL Data Warehouse is a fast, flexible, and secure cloud data warehouse tuned for running complex queries fast and across petabytes of data.
We are excited to announce In-Place restore of disks in IaaS VMs along with simplified restore improvements in Azure Backup. This feature helps roll back or fix corrupted virtual machines through in-place restore without the needs of spinning up a new VM. With the introduction of this feature, customers have multiple choices for IaaS VM restore like create new VM, Restore Disks and Replace disks.
Microsoft data centers and operations centers handling Microsoft Azure, Office 365, and Dynamics 365 have been evaluated by independent auditors as meeting the strong security requirements of the Trusted Information Security Assessment Exchange (TISAX). TISAX is used by European automotive companies to provide a common information security assessment for internal assessments, the evaluation of suppliers, and as an information exchange mechanism.
As part of Azure Machine Learning service general availability, we are excited to announce the new automated machine learning (automated ML) capabilities. Automated ML allows you to automate model selection and hyperparameter tuning, reducing the time it takes to build machine learning models from weeks or months to days, freeing up more time for them to focus on business problems. The making of automated ML was driven by our commitment to improve the productivity of data scientists and democratize AI. By simplifying machine learning, automated ML enables domain experts in the businesses to rapidly build and deploy machine learning solutions.