Learn how to use the Microsoft Graph to harness data and signals from across Microsoft 365. We’ll build a daemon application that uses Azure Functions, Logic Apps, and Microsoft Flow-driven workflows. Then, we’ll drive outcomes using Outlook actionable messages, SharePoint, and Teams. Finally, we’ll discuss best practices for building background applications using app-only authorization.
What if a Walmart engineer could write a letter to their past self, with all the lessons they learned in the first years of a massive digital transformation project? What if you could see it and ask questions? We will focus the insights of this session on two key challenges: 1) Using multi-tenant and ASE application services in building cloud native applications and 2) networking & latency considerations when establishing secure private connections to on-premise resources. The Walmart team will share stories from the Finance data transformation projects and from the Digital Experience parts of their organization. The Azure App Service team will share the latest and greatest networking features in the product and coming soon.
Automated ML is an emerging field in Machine Learning that helps developers and new data scientists with little data science knowledge build Machine Learning models and solutions without understanding the complexity of Learning Algorithm selection, and Hyper parameter tuning. With Azure Machine Learning's automated machine learning capability, given a dataset and a few configuration parameters, you will get a trained high quality Machine Learning model for the dataset that you can use for Predictions. You will learn how CBRE & Walgreen-Boots are using it for productivity gains, empowering domain experts to build ML based solutions and scale to build several models with Azure Machine Learning's automated ML.
Azure Machine Learning is one of the most useful Azure services when dealing with AI workflow. In order to integrate the application with AML pipeline, and to use AML more efficiently, additional steps might be taken. In this talk, the AI Customer engineering team will present a set of ‘Tips and Tricks’ based on customer learnings and early product features.
Heard about the excitement of the cross-platform, multi-database tool known as Azure Data Studio? With the recent announcement for support of PostgresSQL and the release of SQL Notebooks, Azure Data Studio is on the cutting edge of managing data on-prem and on the cloud. If you love typescript and working in open source, you won’t want to miss this session to learn how you can start contributing to Azure Data Studio and even build your own extensions.
Everyone wants to do machine learning, but what does it actually take to make it a reality? Azure Machine Learning service accelerates the end-to-end machine learning lifecycle, enabling data scientists and developers to quickly experiment, iterate, and innovate together. We'll discuss what it takes in practice to do machine learning at scale from data to deployment. We'll walk through an example of how Azure Machine Learning service can speed up each step in your machine learning process from data prep, to model creation, to deployment, to management and finally to monitoring. In doing so, we'll showcase key new features that democratize AI, allow mixed-skill teams to collaborate, and enable ML Ops. We'll share these notebooks so you can start experimenting yourself. With Azure Machine Learning service, you'll be able to *actually* do machine learning.
In this session, we will discuss how to build mission-critical multi-tenant systems on Azure Cosmos DB that scale out to handle a global user base. We’ll begin by covering key concepts surrounding performance isolation, security isolation, high availability, disaster recovery, and SLAs. And then we’ll dive deep by walking through the Cosmos DB design considerations and capabilities – including partitioning strategies and multi-master.
Learn how Bing APIs can help business’s solve real world challenges with scenarios such as fraud detection, plagiarism detection, and extracting sentiments from across the web, as well as how easy it is to infuse applications with contextual intelligence.
Azure DevOps is the ultimate set of DevOps services for any language and any platform. Let's take a tour of each of those services to see how they can help you deliver value to your end users, whether you're a team of one, or an enterprise of thousands. We'll take a look at the source control features of Azure Repos, and how to plan your work with Azure Boards. I'll show you the endless capabilities for continuous integration and continuous delivery with Azure Pipelines, and we'll look at Azure Artifacts for storing your build results, and Azure Test Plans for managing your QA efforts. Along the way, I'll highlight the many integration points that make it easy to work with your existing tools.
The SQL Server 2019 big data cluster platform provides a scalable and enterprise ready technology that can handle data of any sort. Next to the native support for R, Python, Spark, SQL and HDFS, it also provides a way to deploy applications and machine learning models on the cluster. In this session, we will explore various scenarios for doing Machine Learning on big data clusters, including hybrid, and leveraging AI with containerized Cognitive Services.