Join us for a session focused on solutions that consume Microsoft Graph data at scale. We’ll look at generating organizational insights using Microsoft Graph data connect, then examine analytics that drive actions using Microsoft Graph Security API integrations. We’ll also discuss some of the security and privacy challenges that come with use of data at scale, and explore how Office 365 datasets, Azure Storage, and Azure services drive digital transformation.
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.
As developers push intellectual property to registries, how will you secure and protect that IP? Additionally, how do you ensure that once those applications are deployed they properly running and in good shape? In this session we'll cover building container images, image scanning, signing and promotion across environments. Then we will look at the tools and knowledge you need to keep your containerized applications healthy and how to detect when something goes wrong.
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.
You know that Microsoft provides world-class technology, but did you know that web stores, resellers, and sales teams can evangelize, sell, and distribute your products? Join us for an introduction to Microsoft's cloud marketplace, where we'll teach you how to publish to the marketplace, merchandise to the right storefront – AppSource or Azure Marketplace – for your target customer, and the new go to market benefits available from Microsoft.
Bot Framework allows developers to seamlessly build conversational bots which can later be published to services such as Slack, Skype, Messenger and more! However, there might be particular cases in which bots need to be deployed as local services (e.g. Intranets, data compliance, limited external-networks access) and also use language understanding capabilities.Enter Docker containers. Certain Azure Cognitive Services can now be deployed as containers to deliver AI-driven solutions which doesn't send the data to an external network but to an internal server. One of these services is Language Understanding (LUIS), which provides query predictions and whose packages can be integrated in the server for local consumption.In this session, the following scenario will be explored: A cross-platform mobile app which establishes a connection to a bot (which was created by using Bot Framework and which uses a LUIS package produced from utterances and examples) that is used by customers who want to obtain products and services information. Data comes from a local SQL Server database.
We live in a time of internet connected everything. There are now many devices in our home that report information over the internet and can be controlled by our mobile phones. But how do you build one of these?In this session, we look at how to create a prototype interconnected fan unit using an IoT prototyping board. This board will connect to Azure to upload sensor data, and connect to a fan turning it on if the temperature is above a threshold. The threshold will then be made configurable using Azure device twin, allowing it to be easily controlled from a cloud-connected mobile app via Azure Functions. The principles you will see in the session can then be applied to a production system running on appropriate custom hardware.By the end of the session, you will see the power of Azure IoT hub for building internet connected smart devices.
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.
Microsoft Teams is shaping up as the hottest tool on the collaboration block, and bots are shaping up as the way to scale out interaction with complex internal IT systems.Furthermore, the performance of your team relies on the presence of the sweet aroma, taste, and heart-starting impact of caffeinated beverages.Using Microsoft Teams, Microsoft’s Bot Framework and Language Understanding (LUIS) from Azure Cognitive Services, learn how to add a bot to a Team, manage channel and private conversations to ask one of the most critical questions in today’s modern workplace – “who wants a coffee”?
The event-driven promise is helping developers light up new product ideas quickly! Using Azure Functions and event-driven best practices, developers are now able to build in days what usually used to take weeks in the on-premises world. Join us to learn how to enhance your existing cloud applications using serverless technologies and event-driven design patterns. We will share learnings gathered from customers running Functions apps at scale and talk about common pitfalls to avoid.