Microsoft Azure + AI Conference 2019

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Microsoft Azure + AI Conference 2019

Author: Microsoft Azure + AI Conference

The Microsoft Azure + AI Conference brings together the best and brightest from Microsoft and the broader cloud and AI industry in the late fall of 2019 in Las Vegas, Nevada.... Read more

What You Will Learn

  • Microsoft Azure
  • Microsoft AI Platform
  • Azure App Service
  • Azure Machine Learning Service
  • Deep Learning

Pre-requisites

None.

Microsoft Azure + AI Conference Sessions

These keynotes and sessions place an emphasis on AI providing services through the Azure platform.

Microsoft Cloud: Microsoft Azure + AI 2019

by Microsoft Azure + AI Conference

Dec 17, 2019 / 1h 21m

1h 21m

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Description

Join Scott Guthrie for the Microsoft Cloud keynote.

Table of contents
  1. Microsoft Cloud

Microsoft Artificial Intelligence: Microsoft Azure + AI 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 59m

59m

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Description

Join Eric Boyd for the Microsoft Artificial Intelligence keynote.

Table of contents
  1. Microsoft Artificial Intelligence

Azure App Service Overview: Microsoft Azure + AI Conference 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 51m

51m

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Description

Come learn about the recent advancements in Azure App Service from Yutang Lin and Yun Jung Choi! Learn about the newest CI/CD options for App Service, gain an in-depth understanding of the latest security and virtual network integration capabilities, learn how to address common Url routing patterns through integration with API Management’s Consumption tier, and see walkthroughs of the current state-of-the-art for application logging and troubleshooting on the App Service platform.

Table of contents
  1. Azure App Service Overview

Kubernetes, GitHub, and DevOps: Microsoft Azure + AI Conference 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 59m

59m

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Description

By itself, Kubernetes is not necessarily a developer-friendly platform. Building, deploying, and testing microservice-oriented applications can involve a lot of manual work and copious amounts of YAML. Thankfully, Azure has the tools you need to make Kubernetes approachable and productive for developers. In this session, John Stallo and Nicholas Greenfield will cover how you can use tools like Azure Dev Spaces to swiftly onboard developers onto complex applications, and how to rapidly iterate and test applications with dozens or hundreds of microservices. John and Nicholas will also cover how to use Azure Pipelines to increase confidence in code rollouts by testing GitHub pull requests in the context of the broader application running in Azure Kubernetes Service.

Table of contents
  1. Kubernetes, GitHub, and DevOps

What's New in Azure Serverless: Microsoft Azure + AI Conference 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 54m

54m

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Description

Serverless applications are transforming the way developers are solving problems by radically increasing productivity and reducing operational friction. Come learn how easy it is to start and to be production ready with Azure serverless technologies. Sonia Kulkarni will review the various technologies enabling this trend, including Azure Functions, Azure Logic Apps, Application Insights, and more. Sonia will also cover and demo the latest and greatest features, enterprise grade tools and serverless experiences.

Table of contents
  1. What's New in Azure Serverless

Keeping your Azure Apps Healthy and Happy: Microsoft Azure + AI Conference 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 52m

52m

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Description

Working on a web app on App Service or serverless on Azure Functions? Join us to learn tips and tricks that will help you quickly and easily diagnose and resolve issues with your web app or Function app. In this session, you will learn how to effectively leverage App Service Diagnostics for troubleshooting in both proactive and problem-first scenarios. Puneet Gupta and Khaled Zayed will highlight new features in Genie and Navigator by walking through real-world scenarios that will guide you to a cure for common app issues.

Table of contents
  1. Keeping your Azure Apps Healthy and Happy

From Zero to AI Hero: Microsoft Azure + AI Conference 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 46m

46m

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Description

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. In this session with Rachel Kellam, 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.

Table of contents
  1. From Zero to AI Hero

Deep Learning for Developer Crash Course, Part 1: Microsoft Azure + AI Conference 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 1h 6m

1h 6m

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Description

Deep learning is at the center of many exciting application innovations, and, yet, deep learning is also a way you can safely upgrade your existing applications with powerful new capabilities. In this two-part session, Zoiner Tejada does not focus on extremely forward looking innovation with deep learners that will take many years and specialized experts to create - it is not about creating the next self-driving car or champion level video game playing agent. This session IS about the innovations that you, the enterprise developer, can add to your applications today by learning just a few techniques and by having the confidence gained by knowing how such techniques are applied. Most enterprises have challenges with three kinds of data: structured data (think databases and time series), text and images or video. These are all data types for which deep learning has practical uses you can apply today. In this session you will see how to train, evaluate and deploy deep learning models in Azure for each of these three data types, show casing techniques for text analytics/mining, computer vision and forecasting. These are fundamental techniques you will be able to apply to your applications after attending.

Table of contents
  1. Deep Learning for Developer Crash Course, Part 1

Deep Learning for Developer Crash Course, Part 2: Microsoft Azure + AI Conference 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 1h 2m

1h 2m

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Description

Deep learning is at the center of many exciting application innovations, and, yet, deep learning is also a way you can safely upgrade your existing applications with powerful new capabilities. In this two-part session, Zoiner Tejada does not focus on extremely forward looking innovation with deep learners that will take many years and specialized experts to create - it is not about creating the next self-driving car or champion level video game playing agent. This session IS about the innovations that you, the enterprise developer, can add to your applications today by learning just a few techniques and by having the confidence gained by knowing how such techniques are applied. Most enterprises have challenges with three kinds of data: structured data (think databases and time series), text and images or video. These are all data types for which deep learning has practical uses you can apply today. In this session you will see how to train, evaluate and deploy deep learning models in Azure for each of these three data types, show casing techniques for text analytics/mining, computer vision and forecasting. These are fundamental techniques you will be able to apply to your applications after attending.

Table of contents
  1. Deep Learning for Developer Crash Course, Part 2

Develop Apps with Machine Learning in .NET: Microsoft Azure + AI Conference 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 1h 8m

1h 8m

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Description

Get started with ML.NET so you can create machine learning models in C# to run impressive predictions and use them in your .NET applications! Create innovation in your apps with scenarios such as sentiment analysis, anomaly detection, text classification, object detection and many more! ML.NET is an open source and cross-platform Microsoft framework. No matter what OS you use (Windows, Linux, or Mac), we got you covered! ML.NET 1.0 was released on May 2019. Since then, we’ve been releasing a minor version on a monthly cadence. In this session, Cesar De la Torre Llorente will introduce what is ML.NET and Model Builder for Visual Studio and their main scenarios so any .NET developer can get started. Then, we’ll focus on the new features added since v1.0 such as new improvements in Model Builder for Visual Studio, new ML trainers/algorithms added, new native relational database loader for training ML models against databases instead of dataset files, and improvements in Deep Learning with ML.NET.

Table of contents
  1. Develop Apps with Machine Learning in .NET

Developing Microservice-based AspNetCore Applications for Azure: Microsoft Azure + AI 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 1h 2m

1h 2m

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Description

Web applications, APIs, and other back end services that comprise a solution each have characteristics that influence requirements related to microservice-enabled production deployments. This session will provide a focused set of tips to follow during design, development, testing, deployment, and production planning for AspNetCore solutions. Topics covered will include instrumentation, configuration, Docker secrets, development productivity with Docker locally, deployment to Azure leveraging Azure Kubernetes Service (AKS), Azure Container Registry, VSTS automation for CICD, and Azure Key Vault.

Table of contents
  1. Developing Microservice-based AspNetCore Applications for Azure

Delivering AI in Azure from the Command Line with Azure CLI: Microsoft Azure + AI 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 43m

43m

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Description

Join this session with Arun Chandrasekhar to see how development productivity is boosted in the Azure CLI by machine learning generated code. Arun will then showcase how to use the Azure CLI to setup a dev environment to quickly get started with Cognitive Services. Finally, the session will demo how to develop AI apps using the Machine Learning CLI.

Table of contents
  1. Delivering AI in Azure from the Command Line with Azure CLI

Five Services Every Developer Needs to Know: Microsoft Azure + AI 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 59m

59m

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Description

Microsoft Azure is a powerful platform with amazing services available at your fingertips! However, it can also be hard to know which services to pick when you’re getting started with cloud application development. In this session, Scott Hunter and Paul Yuknewicz will cover how to get started with cloud development in Azure using the five most common services that most .NET applications need to run in the cloud.

Table of contents
  1. Five Services Every Developer Needs to Know

Tackling Code Issues with Your App: Microsoft Azure + AI 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 1h 3m

1h 3m

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Description

Triage live site issues using Change Analysis and Azure Monitor Application Insights. Diagnose performance issues using Profiler to identify which lines of code slowed down a web app. No more no repro as snapshot debugger captures the mini dump that includes local variables and call stack to diagnose exceptions. Take snapshot on-demand using Visual Studio to identify where in code threw the exception.

Table of contents
  1. Tackling Code Issues with Your App

Taking a Leap of Faith: AI for the .NET Developer: Microsoft Azure + AI 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 1h 1m

1h 1m

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Description

The landscape of AI and ML technologies provided today by Microsoft is getting bigger and bigger and more complex every day. Azure Machine Learning, Azure Databricks, ML.NET, Cognitive Services, SQL Server, Power BI – to name just a few – are all options when it comes to developing and/or integrating intelligence into modern applications. With many capabilities overlapping in features it becomes increasingly difficult to decide which technology or set of technologies is best suited for a solution, product, or scenario. Understanding in depth Microsoft’s galaxy of AI and ML offerings is not easy, to say the least. The problem becomes even more complex when solution architects and developers are facing the difficult problem of bridging the two worlds – classical software development and data science. Join Ciprian Jichici in this session to explore the fascinating world of AI and ML technologies provided today by Microsoft, from the point of view of the .NET developer. We’ll talk about the existing divide between classical software development and data science and we’ll explore available solutions for closing the gap to help you with the first steps in the fabulous world of data science.

Table of contents
  1. Taking a Leap of Faith: AI for the .NET Developer

Add Intelligence to Serverless with AI on Azure: Microsoft Azure + AI 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 60m

60m

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Description

Serverless is more popular than ever. Many organizations have either augmented their applications with serverless components, or have built entire solutions based on serverless, owing to cost savings and a higher level of abstraction over infrastructure that speeds development and reduces operational overhead. Microservices have been used for years as a way to break up monolithic applications to smaller components that can have separate deployment and scaling requirements, providing greater flexibility for how applications are built and maintained. Artificial Intelligence enables organizations to innovate by using machines to help gain insights on their data and making predictions. In this session, we discuss how all three of these concepts work together to rapidly create and host intelligent solutions at scale. We first cover the basics of building serverless microservices in Azure, then the array of AI options we can use to layer machine learning as a component of the solution. To help you follow along, we will be showing a working serverless microservices solution that will be modified to add in some AI components. By the end of the session with Joel Hulen, you will see how combining AI with serverless opens up many opportunities to make your applications even more awesome, in a way that will scale with (hopefully increased) demand.

Table of contents
  1. Add Intelligence to Serverless with AI on Azure

Add Intelligent Enrichments to Unstructured Data with Knowledge Mining and Cognitive Search: Microsoft Azure + AI 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 1h 1m

1h 1m

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Description

Cognitive search is a technique for using AI to extract additional metadata from images, blobs, and other unstructured data. In this session, Kyle Bunting will review the process for intelligently extracting information from opaque documents using an Azure Search indexing pipeline. Pre-built and custom cognitive skills are used to enrich the search index, making the content more searchable and usable.

Table of contents
  1. Add Intelligent Enrichments to Unstructured Data with Knowledge Mining and Cognitive Search

Gain Efficiencies and Reliability with Azure: Microsoft Azure + AI 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 1h 0m

1h 0m

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Description

See how to leverage PaaS and IaaS services on Azure to move your applications to cloud. This session with Jay Schmelzer covers common real-world scenarios, patterns and best practices for how to leverage Azure services to host various component in your code and explains the benefits for modernize your application.

Table of contents
  1. Gain Efficiencies and Reliability with Azure

Re-inventing Computing - The Challenges of Quantum Algorithms: Microsoft Azure + AI 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 1h 2m

1h 2m

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Description

We’re in the dawn of a new era in computing – Quantum Computing (QC), an approach based on the very spectacular and strange behavior of matter and energy governed by the laws of quantum physics. This amazing new direction promises the much-needed support for significant advancements in key areas like nitrogen fixation, carbon capture, materials science, and machine learning, to name just a few. There is a lot going on right now in QC with a lot of energy and money being invested by some of the largest players in IT. Yet, it’s not all straightforward and simple, and the promises are by no means certainties. Au contraire, the challenges posed by QC are monumental. They range from the physical building of quantum computers all the way to designing completely new and ground-breaking algorithms. The strange and counterintuitive behavior of quantum physics has a dramatic impact on the new kind of logic that we need to adopt to program quantum computers. Join Ciprian Jichici in this session to explore the fascinating world of quantum algorithms. We’ll start with a brief introduction on QC and then we’ll discuss the challenges of coding in a world that is radically different from the one we’re used to. Welcome to the strange and fascinating world of Quantum Computing!

Table of contents
  1. Re-inventing Computing - The Challenges of Quantum Algorithms

Deploying and Using ML Models across the AI Spectrum: Microsoft Azure + AI 2019

by Microsoft Azure + AI Conference

Dec 11, 2019 / 1h 1m

1h 1m

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Description

The use of AI and ML models has become common practice in modern technological solutions. Understanding how to leverage AI and ML has never been more important. The AI spectrum encompasses everything from pre-built AI, such as Cognitive Services, to fully custom ML models. In this session, Kyle Bunting will review AI and ML at each level of the AI spectrum and discuss various methods for deploying and using those models in applications.

Table of contents
  1. Deploying and Using ML Models across the AI Spectrum