Learn more about Service Fabric, the microservices platform that powers Azure. Customers will show how they use Service Fabric to build, scalable, mission critical applications using the native programming models and Windows Containers, supported with published application architecture guidance. This session will also dive into the future direction of Service Fabric Mesh, the serverless, cloud-native, container based application platform.
Discover the latest updates about Project Personality Chat – Project Personality Chat enhances your bot’s conversational capabilities by handling small talk, in line with a chosen personality. Choose a personality that aligns with your brand's voice, by choosing from available default personas.
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.
In this session, we will look at a new class of purpose-built cloud platform capabilities, including Azure Policy, Blueprints & Management Groups, that address the need for control & governance at scale without sacrificing developer agility while providing guardrails & visibility for organizations to comfortably build in the cloud for devops centric environments
Spark is the world’s foremost distributed analytics platform, delivering in-memory analytics with a speed and ease of use unheard of in Hadoop. Azure Cosmos DB is the lighting fast distributed database powering Fortune 500 companies like Walmart, Exxon Mobile, Toyota and many others. Did you know you can combine them easily using our natively built azure-cosmosdb-spark connector or now you can use the new Spark API feature integration that allows Spark to fully take advantage of Cosmos DB to run real-time analytics directly on petabytes of operational data! In this session we’ll go over some of the most common use cases of the azure-cosmosdb-spark connector and highlight how to avoid the most common pitfalls. We will talk about the new Azure Cosmos DB Spark API and the native support it brings for Apache Spark engines executing directly on petabytes of operational data stored in your globally distributed Cosmos databases. We will walk through the capabilities Spark API brings to developers, data engineers and data scientists such that they can use Cosmos DB as a flexible, scalable, and performant planet-scale data platform for running both OLTP and HTAP workloads alike.
Like most developers, we (Cecil Phillip and Burke Holland) have been curious, skeptical and admittedly more than just a bit confused about Serverless. So we decided to build something with it. Not just a demo; a real application used by real people. It was great fun, we learned a lot, and got banged up a little bit on the way. In this talk you'll see how we built a full stack application using a serverless approach with Azure Functions. We'll show you some of the technical walls with an imprint of our faces still on them, and a few of the more "OMG" moments where Serverless really starts to make sense. This project was a lot of fun. It was also confusing and difficult - but the journey was worth it and we would love to share it with you.
For many newcomers to Azure Cosmos DB, the learning process starts with data modeling and partitioning. How should I structure my data? When should I co-locate data in a single container? Should I de-normalize or normalize properties? What’s the best partition key for my model? In this demo-filled session, we discuss the strategies and thought process one should adopt for modeling and partitioning data effectively in Azure Cosmos DB. Using a real-world example, we explore Cosmos DB key concepts – request units (RU), partitioning, and data modeling – and how their understanding guides the path to a data model that yields best performance and scalability.
Many say data is the new oil, but electricity may be a better analogy. Data powers insights behind critical business decisions but must also be handled safely, properly harnessed, and effectively delivered. Microsoft’s unique combination of Power BI and Azure SQL Data Warehouse (SQL DW) enables customers everywhere to use industry-leading services not only to power their businesses with rich insights, but also store and deliver that data with unmatched efficiency and performance. Join us to learn how Power BI and SQL Data Warehouse empowers your organization to analyze trillions of rows of data and extract instant insights, leveraging Power BI aggregations and composite models to unify data from multiple connections with surprising ease.
Have developer skills but want to scale up to data science in Azure? Azure Notebooks makes it easy to move from developer to including data science skills in your skill set. Microsoft provides an Azure hosted Jupyter Notebook solution with Azure Notebooks, which offer minimal overhead and opportunities for collaboration. Azure Notebooks provides execution environments for Python 2, Python 3, F#, and R. In this session, you will learn about the power and flexibility of Azure Notebooks, see them in action, and why it’s useful to run them on Microsoft Azure.
AI, machine learning, deep learning, and advanced analytics are being infused into every team and service at Microsoft—understanding customers and the business, operating services, and delivering innovative new features. But doing machine learning at the scale of Microsoft is challenging. ONNX (open neural network exchange format) has bridged the different model formats for ML frameworks (e.g. TensorFlow, Pytorch, MXNet) to a single execution environment with the ONNX Runtime. Learn how Bing, Ads, Speech, Office, Cognitive Services, and others use frameworks like TensorFlow, PyTorch, Scikit-learn, Caffe for training and rely on ONNX Runtime for high performance inferencing. You’ll also learn how to use ONNX and ONNX Runtime in your AI application with Azure ML.
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