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.
Vipps released their payment app on May 30th, 2015. Five months later, they had exceeded a million active users. How in the world did they handle this explosive growth? Join Vipps and the API Management team as they do a technical breakdown on the journey from zero users to millions, and how Vipps used API Management, Azure DevOps, and Azure Kubernetes Service to transform their business from an on-premise monolith, to a microservice driven architecture, as seamlessly as possible. Discover what to do and what not to do when scaling your services, and how to use API Management and Azure Kubernetes Service together to create a highly scalable system that provides frictionless experiences for your customers.
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.
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.
The holy grail of the Internet of Things is the ability to easily distribute the intelligence of your application across the Cloud and the Edge. Being able to run analytics, AI or store data at the Edge addresses many common and key enterprise IoT scenarios. Come learn how to easily create deployments for IoT devices that include AI, Machine Learning, Stream Analytics, as well as your own custom code on devices smaller than a Raspberry PI.
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.
Transforming classic applications, which were built across multiple years, cannot be pushed to the cloud overnight. Uplifting client UI often requires access to backend web services and/or databases. This session will guide you through how to expose an internal web service to the cloud and have a SharePoint library webhook call the web service. This session will review the required Azure configuration settings, overall architecture required, Web Service C# code, and have the web service answer a SharePoint Webhook.
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.
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.
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.
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