Learn how Azure Data Explorer service can help you build near real-time and complex analytical solution. We will cover the service overview and demonstrate the querying capabilities on terabytes of data.
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.
From smart sensors and actuating devices to integrated robotic systems, industrial organizations are employing increasingly sophisticated automation technologies to drive efficiency and productivity. Microsoft is accelerating the journey toward a world where machines operate in more dynamic and intuitive ways. From smart buildings, to industrial machinery, to robotics, Microsoft is democratizing the development of increasingly autonomous systems by providing domain experts, developers, and data scientists with the tools they need to seamlessly develop and manage autonomous systems.
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.
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.
Serverless vs Containers, Cost vs Performance, Tabs vs Spaces. These are just a few of the many questions every developer comes to terms with when choosing to host their application the cloud. The good news? It may not be as binary as it seems. Join Jeff Hollan is this live session as he showcases common cloud architectures around Kubernetes, Containers, and Serverless. Understand the benefits and drawbacks of each, and see how you can take advantage of the full spectrum of cloud native computing to build applications faster.
Wegmans re-invented what it means to have a meaningful shopping experience at the grocery store. Join us for this session as we dive deep into the technical implementations, solutions, and techniques that fueled Wegmans grow to the front of the pack. Discover how Wegmans launched a brand-new food delivery app in under 12 weeks by leveraging API first architecture and unlocked new avenues for research-and-development by exposing internal services as APIs. Follow along with Wegmans while learning how to get started building with APIs using a hybrid approach, create DevOps pipelines for your APIs, and effectively use API Management policies to expose APIs in a secure manner.
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.
In this session, we'll discuss why to use the Common Data Service (CDS) for building business apps vs. starting from scratch. From there, we'll dig into how you can use the Common Data Service as a part of your Azure application. We'll look at core CDS capabilities, as well as dive into the CDS APIs and SDKs.
Can your applications scale as needed? Can you continuously deliver mission-critical applications while reducing risks? In this session, we’ll demonstrate some new, exciting Azure Resource Manager capabilities including deployment orchestration, health integration during deployments, and other features that dramatically reduce the risk associated with large-scale service rollouts. We’ll review Virtual Machine Scale Sets (VMSS) - one of the fastest growing services in Azure - powering many mission-critical applications. And we’ll take a deeper look at some important configuration choices and related trade-offs, explain why and when you should use them — with actual customer scenarios — and what you should expect as a result.