Machine learning development has new complexities beyond software development. There are a myriad of tools and frameworks which make it hard to track experiments, reproduce results and deploy machine learning models. Learn how you can accelerate and manage your end-to-end machine learning lifecycle on Azure Databricks using MLflow and Azure Machine Learning to reliably build, share and deploy machine learning applications using Azure Databricks.
Join us to deep-dive and learn how we’re making it easy to apply AI to create structure from your data using the new Knowledge Store and Complex Type capabilities built into Azure Search. The Knowledge Store lets you shape your AI enriched data into almost any form for analytics, machine learning or application processing. Complex Types then lets you take shaped data and build advanced data exploration experiences. In this session you'll learn how to leverage these Knowledge Mining capabilities to create new experiences and extract insights from your data.
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.
Application streaming is becoming a more common scenario for developers. We'll discuss how you can adjust your app to work well with remote streaming and media streaming scenarios. We’ll also cover how you can use this technology to improve your testing scenarios.
Azure Resource Manager (ARM) templates is one of the most widely used tools in the Azure ecosystem, enabling Infrastructure-as-Code, deployment automation, repeatability, compliance, and standardization. In this session we'll cover the latest improvements and best practices for Azure ARM customers. We'll cover new features which include ARM Authoring tooling enhancements, predictable deployments, and investments to the overall deployment platform.
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.
How can you bring IoT to the billions of devices already in the field without creating a large security risk? Connecting your enterprise equipment represents an opportunity to unlock new scenarios that increase efficiency and revenue, while decreasing costs and equipment downtime. In this session, we’ll demonstrate the end-to-end process of adding Azure Sphere to business critical equipment to ensure devices connected to the cloud stay secured and hear directly from Starbucks on how they’re using Azure Sphere to securely connect existing equipment. You’ll learn about Visual Studio tools for Azure Sphere and how to connect Azure Sphere to Azure IoT Central in minutes to view and take action on data and telemetry.
Ever wondered what breed that dog or cat is? Let’s build a pet detector to recognize them in pictures! We will walk through the training, optimizing, and deploying of a deep learning model using Azure Notebooks and the Azure Machine Learning service. We will use transfer learning to retrain a MobileNet model using TensorFlow to recognize dog and cat breeds using the Oxford IIIT Pet Dataset. Next, we’ll optimize the model using Azure Machine Learning service to improve the model accuracy. Putting on our developer hat, we'll then refactor the notebooks into Python modules using Visual Studio Code. Finally, we will deploy the model as a web service in Azure. See how Azure and Visual Studio Code has made AI and machine learning easy.
Join Mark Russinovich, Azure CTO, to learn how Microsoft’s Azure enables intelligent, modern and innovative applications at scale in the cloud, on-premises and on the edge. Microsoft Azure has achieved massive, global scale, with more than 50 announced regions consisting of over 100 datacenters, and it is growing fast. It delivers the promise of cloud computing, including high-availability, extreme performance, and security, by custom designing software and hardware to work best together. Mark takes you on a tour of Azure’s datacenter architecture and implementation innovations, describing everything from Azure’s global infrastructure, to how we enable large-scale enterprise scenarios on both cloud and edge, to how we bring quantum computing to real-world scenarios today, and more.
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.
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