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Designing an Intelligent Edge in Microsoft Azure

This course targets software developers looking to integrate AI solutions in edge scenarios ranging from an edge data center down to secure microcontrollers. This course will showcase how to design solutions using Microsoft Azure.

Cloud computing has moved more and more out of the cloud and onto the edge. In this course,

Designing an Intelligent Edge in Microsoft Azure, you will learn foundational knowledge of edge computing, its intersection with AI, and how to utilize both with Microsoft Azure. First, you will learn the concepts of edge computing. Next, you will discover how to create an edge solution utilizing Azure Stack, Azure Data Box Edge, and Azure IoT Edge. Finally, you will explore how to utilize off-the-shelf AI and build your own for Azure IoT Edge. When you are finished with this course, you will have the skills and knowledge of AI on the edge needed to architect your next edge solution. Software required: Microsoft Azure, .NET

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Edge Computing Azure Stack Azure Data Box Edge Azure IoT Edge AI on the Edge AI Pipeline Components Custom Components

What's inside

Syllabus

Course Overview

Designing Solutions That Incorporate AI Pipeline Components on Edge Devices

Identify Appropriate Tools for the Solution

Determine When to Use Built-in Components

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Teaches foundational knowledge of edge computing, its intersection with AI, and how to utilize both with Microsoft Azure

Develops skills in designing solutions using Microsoft Azure

Leverages the expertise of instructors Jared Rhodes in the field of edge AI solutions

Suitable for software developers looking to integrate AI solutions in edge scenarios, from edge data centers to secure microcontrollers

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Reviews summary

Architecting ai on azure edge

According to learners, this course offers a solid foundation in designing intelligent edge solutions with Microsoft Azure. Students frequently highlight the course's effectiveness in clarifying complex edge computing concepts and demonstrating their integration with AI using Azure Stack, Data Box Edge, and Azure IoT Edge. The content is praised for its practical relevance to software developers looking to integrate AI into edge scenarios. Some feedback suggests that while it provides a strong overview, learners benefit most if they come with a foundational understanding of Azure and .NET, and that hands-on lab exercises are crucial for solidifying the theoretical knowledge. Overall, it's considered a valuable resource for architects and developers aiming to build next-generation edge solutions.

Well-paced for developers; might need supplementary study.

"The pace was generally good for a developer looking to get up to speed on intelligent edge."

"I felt some topics could have gone deeper, but it serves as a solid starting point."

"For a foundational course, the coverage is comprehensive, but advanced users might seek more in-depth resources elsewhere."

Benefits from prior Azure/ .NET knowledge; hands-on practice is essential.

"Coming in with some Azure background definitely made the course easier to follow and apply."

"I found that the hands-on labs were critical for solidifying the theoretical aspects discussed."

"While the course provides good theory, I needed to actively practice the concepts in my own Azure environment."

Focuses on real-world application of AI in edge environments.

"The examples of integrating off-the-shelf AI and building custom models for IoT Edge were very helpful."

"I appreciate how the course showed practical ways to deploy AI pipelines onto edge devices."

"It's highly relevant for developers aiming to implement intelligent solutions beyond the cloud."

Provides a strong grounding in edge computing and Azure services.

"I gained a clear understanding of edge computing fundamentals and how Azure services like IoT Edge fit in."

"The course really helped me grasp the concepts behind Azure Stack and Data Box Edge for edge scenarios."

"It's a great introduction to applying AI capabilities directly at the edge, using Microsoft's ecosystem."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Designing an Intelligent Edge in Microsoft Azure with these activities:

Organize and Review Course Materials

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Helps you stay organized and reinforces your understanding of the course concepts.

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Create a dedicated folder for the course materials.
Organize the materials into logical categories.
Review the materials periodically to reinforce your learning.

Follow the Microsoft Learn Path on Azure AI Fundamentals

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Provides a structured and guided approach to learning the basics of Azure AI services, complementing the concepts covered in the course.

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Create a Microsoft Learn account.
Enroll in the Azure AI Fundamentals learning path.
Complete the modules and hands-on exercises.

Review 'Designing Data-Intensive Applications' by Martin Kleppmann

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Provides insights into designing data-intensive applications, which is essential for building scalable and reliable solutions on Azure.

View Secret Colors on Amazon

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Read the first three chapters to gain a foundational understanding of data modeling and distributed systems.
Focus on chapters 4-6 to learn about data storage and query optimization techniques.
Review the case studies in chapters 7-9 to see how these concepts are applied in real-world scenarios.

Five other activities

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Solve Data-Related Coding Challenges on LeetCode

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Strengthens your problem-solving skills and ability to work with data structures, which is essential for developing AI solutions.

Browse courses on Data Structures

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Choose LeetCode problems related to data structures and algorithms.
Solve the problems using your preferred programming language.
Review your solutions and identify areas for improvement.

Build a Simple IoT Solution Using Azure IoT Hub

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Hands-on experience in building an IoT solution will solidify your understanding of the concepts covered in the course.

Browse courses on Azure IoT Hub

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Create an Azure IoT Hub instance.
Connect a device simulator to the IoT Hub.
Send telemetry data from the device simulator to the IoT Hub.
Monitor the telemetry data using the Azure IoT Explorer.

Attend an Online Study Group with Fellow Students

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Engages you in collaborative learning, allowing you to share insights and learn from others.

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Find a study group or create your own.
Set regular meeting times.
Discuss course topics, share knowledge, and work on assignments together.

Design an Azure IoT Edge Solution Architecture

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Tests your ability to apply the principles of edge computing and AI to design a practical solution.

Browse courses on IoT

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Identify the use case and requirements for your IoT solution.
Select the appropriate Azure IoT Edge services and devices.
Design the data flow and processing pipeline.
Create a deployment plan for your solution.

Present a 10-minute Overview of Edge Computing and AI

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Helps you synthesize and communicate your knowledge of edge computing and AI, reinforcing your understanding of the key concepts.

Browse courses on Edge Computing

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Gather information from the course materials and additional research sources.
Organize the information into a logical flow.
Create visual aids, such as slides or diagrams, to enhance your presentation.
Practice your delivery to ensure clarity and effectiveness.

Career center

Learners who complete Designing an Intelligent Edge in Microsoft Azure will develop knowledge and skills that may be useful to these careers:

AI Engineer

An AI Engineer designs, develops, and deploys artificial intelligence models. They have a strong understanding of machine learning, deep learning, and other AI techniques. Designing an Intelligent Edge in Microsoft Azure may be useful for an AI Engineer because it provides a foundation in AI and edge computing, which are both important technologies for AI Engineers.

See salaries and explore the career path for AI Engineer

Data Scientist

A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. Designing an Intelligent Edge in Microsoft Azure may be useful for a Data Scientist because it provides a foundation in AI and edge computing, which are both important technologies for Data Scientists.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models. They have a strong understanding of machine learning, deep learning, and other AI techniques. Designing an Intelligent Edge in Microsoft Azure may be useful for a Machine Learning Engineer because it provides a foundation in AI and edge computing, which are both important technologies for Machine Learning Engineers.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

A Software Engineer designs, develops, and maintains software systems. They have a strong understanding of computer science fundamentals, software design principles, and programming languages. Designing an Intelligent Edge in Microsoft Azure may be useful for a Software Engineer because it provides a foundation in AI and edge computing, which are both important technologies for Software Engineers.

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Cloud Architect

A Cloud Architect designs, develops, and manages cloud computing solutions. They have a strong understanding of cloud computing concepts, technologies, and best practices. Designing an Intelligent Edge in Microsoft Azure may be useful for a Cloud Architect because it provides a foundation in AI and edge computing, which are both important technologies for Cloud Architects.

See salaries and explore the career path for Cloud Architect

DevOps Engineer

A DevOps Engineer automates and streamlines the software development and deployment process. They have a strong understanding of software development, IT operations, and automation tools. Designing an Intelligent Edge in Microsoft Azure may be useful for a DevOps Engineer because it provides a foundation in AI and edge computing, which are both important technologies for DevOps Engineers.

See salaries and explore the career path for DevOps Engineer

Data Architect

A Data Architect designs, develops, and manages data architectures. They have a strong understanding of data modeling, data integration, and data governance. Designing an Intelligent Edge in Microsoft Azure may be useful for a Data Architect because it provides a foundation in AI and edge computing, which are both important technologies for Data Architects.

See salaries and explore the career path for Data Architect

Security Architect

A Security Architect designs, develops, and manages security architectures. They have a strong understanding of security concepts, technologies, and best practices. Designing an Intelligent Edge in Microsoft Azure may be useful for a Security Architect because it provides a foundation in AI and edge computing, which are both important technologies for Security Architects.

See salaries and explore the career path for Security Architect

Network Architect

A Network Architect designs, develops, and manages network architectures. They have a strong understanding of network concepts, technologies, and best practices. Designing an Intelligent Edge in Microsoft Azure may be useful for a Network Architect because it provides a foundation in AI and edge computing, which are both important technologies for Network Architects.

See salaries and explore the career path for Network Architect

System Administrator

A System Administrator manages and maintains computer systems and networks. They have a strong understanding of system administration concepts, technologies, and best practices. Designing an Intelligent Edge in Microsoft Azure may be useful for a System Administrator because it provides a foundation in AI and edge computing, which are both important technologies for System Administrators.

See salaries and explore the career path for System Administrator

Database Administrator

A Database Administrator manages and maintains databases. They have a strong understanding of database concepts, technologies, and best practices. Designing an Intelligent Edge in Microsoft Azure may be useful for a Database Administrator because it provides a foundation in AI and edge computing, which are both important technologies for Database Administrators.

See salaries and explore the career path for Database Administrator

IT Manager

An IT Manager plans, directs, and coordinates the activities of an IT organization. They have a strong understanding of IT management concepts, technologies, and best practices. Designing an Intelligent Edge in Microsoft Azure may be useful for an IT Manager because it provides a foundation in AI and edge computing, which are both important technologies for IT Managers.

See salaries and explore the career path for IT Manager

Product Manager

A Product Manager plans, develops, and markets products. They have a strong understanding of product management concepts, technologies, and best practices. Designing an Intelligent Edge in Microsoft Azure may be useful for a Product Manager because it provides a foundation in AI and edge computing, which are both important technologies for Product Managers.

See salaries and explore the career path for Product Manager

Project Manager

A Project Manager plans, executes, and closes projects. They have a strong understanding of project management concepts, technologies, and best practices. Designing an Intelligent Edge in Microsoft Azure may be useful for a Project Manager because it provides a foundation in AI and edge computing, which are both important technologies for Project Managers.

See salaries and explore the career path for Project Manager

Business Analyst

A Business Analyst analyzes and solves business problems. They have a strong understanding of business analysis concepts, technologies, and best practices. Designing an Intelligent Edge in Microsoft Azure may be useful for a Business Analyst because it provides a foundation in AI and edge computing, which are both important technologies for Business Analysts.

See salaries and explore the career path for Business Analyst

Reading list

We've selected seven books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Designing an Intelligent Edge in Microsoft Azure.

TinyML

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Introduces TinyML, a specialized field of machine learning for microcontrollers and embedded devices. It offers practical guidance on implementing AI models on resource-constrained edge devices.

TinyML: Machine Learning with TensorFlow Lite on...

Paperback

O'Reilly.TinyML. Wykorzystanie TensorFlow Lite do...

Paperback

$$$

TinyML: Machine Learning with TensorFlow Lite on...

Kindle Edition

Site Reliability Engineering

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While not specific to edge computing, this book provides a comprehensive overview of SRE practices, which are essential for ensuring the reliability and availability of edge solutions.

Site Reliability Engineering: How Google Runs...

Kindle Edition

Architecting for Scale

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Explores architectural patterns and best practices for scaling web applications, which key consideration for deploying AI solutions on the edge at scale.

Architecting for Scale: How to Maintain High...

Paperback

Architecting for Scale: High Availability for Your...

Paperback

Architecting for Scale: How to Maintain High...

Kindle Edition

Building Microservices

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Delves into the principles and practices of microservices architecture, which common approach for building scalable and maintainable edge solutions.

Building Microservices: Designing Fine-Grained...

Paperback

Building Microservices: Designing Fine-Grained...

Paperback

Building Microservices: Designing Fine-Grained...

Spiral-bound

$$$

Building Microservices

Kindle Edition

Parallel Programming

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While not directly related to edge computing, this book provides a solid foundation in parallel programming techniques, which are essential for optimizing AI models and edge applications for performance.

Parallel Programming: Concepts and Practice

Kindle Edition

$$$

Microservices Patterns

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Explores cloud native patterns and practices that are applicable to edge computing scenarios. It provides valuable insights into designing and implementing scalable, resilient, and efficient edge solutions.

Microservices Patterns: With examples in Java

Paperback

Microservices Patterns: With examples in Java

Kindle Edition

Designing Data-Intensive Applications

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Provides a solid foundation in data modeling and distributed systems design. It can serve as a valuable background resource for understanding the underlying principles of data management and processing on the edge.

Designing Data-Intensive Applications: The Big...

Paperback

Designing Data-Intensive Applications: The Big...

Kindle Edition

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