Azure Artificial Intelligence Services
Design for IoT
Design Messaging Solution Architectures
Design Media Service Solutions
The course is part of these learning paths
The Microsoft Azure 70-535 exam has a large section focused on creating practical solutions using Azure technologies. About 10-15% of the exam will cover Azure products focused on AI, Messaging, Internet of Things, and Video Media. This will require familiarity with dozens of Azure solutions.
This course will take you through all of the relevant technologies and ensure you know which ones to pick to solve specific problems. After taking this course you should be well-prepared for the 70-535 exam. However this is not only a test prep course. This course is also for developers, engineering managers, and cloud architects looking to get a better understanding of Azure services.
Whether your app deals with artificial intelligence, managing IoT devices, video media, or push notifications for smart phones - Azure has an answer for every use case. This course will help you get the most out of your Azure account by preparing you to make use of many different solutions.
Design solutions using Azure AI technologies
Design solutions for IoT applications using Azure technologies
Create a scalable messaging infrastructure using Azure messaging technologies
- Design media solutions using Azure media technologies and file encoding
People who want to become Azure cloud architects
People preparing for Microsoft’s 70-535 exam
General knowledge of IT architecture
Azure Media Services is your base of operations for managing video media. It will handle storage, encoding, and packaging video for streaming delivery. To use it you need to create an Azure Media Services account. This is as simple as a few clicks in the Azure web portal.
As you can see from the diagram Media Services manages video content in five parts. First there is upload and storage for administering content at rest. Then there is the encoding step to prepare content for media players. Next is setting asset delivery policies for clarifying how content can be used. From there, content is published to an OnDemand locator, and finally, content can be streamed to clients. Mixed into those five steps are some important security decisions. In this diagram we add those in. There is setting the encryption content key and an authorization policy. Together these allow for content to be dynamically encrypted during playback. Media Services also includes options for encrypting content at rest.
Azure Media Services supports a number of approaches to delivering content. You can create streaming endpoints for video content using multi-bitrate (adaptive bitrate for unstable network conditions) streams over HLS, HTTP Live Streaming, or MPEG-DASH. You can create URL endpoints for both streaming or download. It’s up to you to decide whether you want users to be able to copy your media files, play them in browsers, or stream them in smart phones and other devices.
Honestly it would take its own course to cover every single thing Media Services can do. See the documentation if you really want to go down a rabbit hole. One very important thing I will leave you with regarding Media Services, however, is that you can interact with its functionality in multiple ways. Like other Azure products Media Services works with the Azure web dashboard, a REST API, and has an SDK for .NET language support. So regardless of how you want to access your Media Services account, you will always have options.
Now, an important related service is the Azure Video Indexer product. Video Indexer replaces the older Video API service Azure supported in the past. Video Indexer is a relatively straightforward set of tools built on top of Azure Media Analytics, which we will cover later. It is designed to extract information from videos for easier management. So for example you can use Video Indexer to do visual text recognition on a video, transcribe audio, detect faces, translate the video to another language, and much much more. See the list of features in the slide here to get a full sense. Important to note: These tools are also supported by the SDK and can be accessed using .NET or REST calls.
Finally, the Azure Computer Vision API is somewhat similar to the image classification tools we discussed in the section on AI. This API gives developers tools for processing images using Azure’s advanced classification algorithms. You can access the API using languages like Python, C#, Ruby, Java, and PHP, or even just use cURL. Assuming your image meets the requirements shown in the slide, you can then call the API to do things like recognize text, flag adult content, apply tags based on content, generate descriptions, detect human faces, and even recognize domain-specific content.
So there you have it - we have gone over most of the basic media technologies in the Azure software ecosystem. We still have one major component to cover - the Azure Media Analytics tool set. We will cover that and briefly discuss file-based encoding systems in Azure in the next lesson. See you there.
About the Author
Jonathan Bethune is a senior technical consultant working with several companies including TopTal, BCG, and Instaclustr. He is an experienced devops specialist, data engineer, and software developer. Jonathan has spent years mastering the art of system automation with a variety of different cloud providers and tools. Before he became an engineer, Jonathan was a musician and teacher in New York City. Jonathan is based in Tokyo where he continues to work in technology and write for various publications in his free time.