Welcome to Azure Hybrid, Multicloud, and Edge Day--please join us for the digital event. Today, we're sharing how Azure Arc extends Azure platform capabilities to datacenters, edge, and multicloud environments through an impactful, 90-minute lineup of keynotes, breakouts, and technical sessions available live and on-demand. Now you can build, train, and deploy your machine learning models right where the data lives, such as your new or existing hardware and IoT devices. When I talk with customers, one of the things I hear most frequently is how new cloud-based applications drive business forward. And as these new applications are built, they need to take full advantage of the agility, efficiency, and speed of cloud innovation. However, not all applications and infrastructure they run on can physically reside in the cloud.

Azure Integration Services consisting of Logic Apps, API Management, Event Grid, and Service Bus helps customers connect applications, data, and services on-premises and in the cloud, helping enterprises create new revenue opportunities with an API-driven partner and developer ecosystem and boost productivity with secure and automated workflows. Our vision is to empower all kinds of organizations and users, from citizens to professional developers, to use Azure Integration Services to enable a composable enterprise. We believe that integration is an essential part of modern applications and should be available and accessible to all kinds of users; citizens to professional developers. We reach out to developers at the platform of their choice with Visual Studio and Visual Studio Code extensions, support for Azure DevOps, GitHub Actions, Application Insights, and Azure Monitor, offering an intuitive user experience and increasing developer productivity. With Power Automate and its Windows-integrated robotic process automation (RPA) capabilities, we enable citizen developers to leverage integration without writing code.

Cluster administrator privileges are needed to create labels for cluster nodes. If this property is specified, training jobs are scheduled to run on nodes with the specified node labels. You can use nodeSelector to target a subset of nodes for training workload placement. This can be useful in scenarios where a cluster has different SKUs, or different types of nodes such as CPU or GPU nodes. For example, you could create node labels for all GPU nodes and define an instanceType for the GPU node pool.

Azure Arc is Microsoft's hybrid solution for getting the simplicity and value of cloud services on any infrastructure, by putting a representation of that infrastructure in Azure so the automation, monitoring and policy tools that work in the cloud can manage it too. Arc started with servers, VMs, Kubernetes and SQL Server databases -- an on-premises equivalent of IaaS -- and moved on to bring cloud PaaS services to the infrastructure you manage with Arc, starting with putting Azure Data Services on Kubernetes containers. The next Azure service to come to Arc is machine learning, which you can now use to run training locally on the data in the databases you're managing with Arc. That could be data in a different cloud that you don't want to copy to Azure, incurring data egress costs and latency, data you want to keep in your own data center for regulatory reasons, or data you want to process at the edge so you can act on it immediately. Arc-enabled Machine Learning will be useful at the edge for workloads like predictive maintenance, monitoring assets in the field for failure or analyzing activity in retail locations, where you don't have the connectivity or bandwidth to use cloud services.

This week at Microsoft Ignite, a number of new developments to Azure were in focus. While there were dozens of updates to the world's second-largest public cloud, data was once again in the spotlight. The company made a series of announcements to enable users to extract more value from the exponential increase in data. Satya Nadella, in his Ignite keynote, provided a new visionary direction, or at least a new way of expressing the company's cloud endeavors. In short, the Microsoft cloud is evolving to further embrace edge, privacy, security, AI, and developers (both coders and no coders), and to serve as an engine of job creation. On the surface, this shift appears subtle.

If there's a silver lining to social distancing, it's the fact that it gives us a chance to catch up on content we otherwise might have missed. There are always too many sessions to attend at cloud conferences -- from service introductions and updates to best practices and use cases -- that could change the way you use cloud technologies. The global health crisis has made it unlikely any of us will gather for a conference in 2020. Given the dangers of COVID-19, it seems unwise for thousands of professionals from around the world to gather in a crowded convention center. While the in-person conference experience is off the table for the near future, there are plenty of resources still available to review from cloud conferences over the past year.

Microsoft CEO Satya Nadella imagines a world with an ever-expanding set of connected devices that process data locally and work in tandem with the cloud – and his company has designed its entire multibillion-dollar cloud business around that concept. Nadella revealed the company's strategy for edge computing during Microsoft's recent shareholders meeting. Edge computing is a buzzword, but it basically means processing data on the devices themselves, instead of offsite in the cloud. Think of a self-driving car. It needs to be able to process data and make split-second decisions without the delays that would come if that data had to be processed far away in the cloud.

Microsoft is emphasizing the importance of a consistent management platform across cloud environments, with CEO Satya Nadella touting the capabilities of Azure during his keynote at Ignite 2019 in Orlando on Monday. Azure Arc, announced during the keynote, is "a control plane built for multicloud, multi-edge, and for the first time managed data services for where the edge compute is," Nadella said. The system allows organizations to manage non-Azure equipment, including private cloud infrastructure, edge devices, and resources on Google Cloud Platform and Amazon Web Services (AWS) identically to the way Azure resources are managed. "We want every tech company to be a tech company in its own right," Nadella said, emphasizing the importance of enabling organizations to leverage their business data, noting that 73% of business data is not analyzed. SEE: Microsoft Azure: An insider's guide (free PDF) Azure Synapse is capable of running queries across structured and unstructured data, with a sample query taking nine seconds to complete in Azure, compared to 11 minutes in Google Cloud, with Synapse capable of handling queries from 10,000 users, while Google BigQuery and Amazon RedShift are throttling requests.