Edge AI: Tools and Best Practices for Building AI Applications at the Edge

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Kumaran Ponnambalam

1:18:31

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01 - Introduction

01 - Why AI at the edge.mp4

00:45

02 - 1. Edge Computing and Artificial Intelligence

01 - Internet of Things.mp4

03:04

02 - What is edge computing.mp4

04:40

03 - Benefits of edge computing.mp4

03:10

04 - Challenges of edge computing.mp4

03:19

05 - AI at the edge.mp4

02:27

06 - Benefits and challenges of edge AI.mp4

02:11

03 - 2. Edge AI Use Cases

01 - Personal edge AI.mp4

02:39

02 - Retail edge AI.mp4

02:10

03 - Healthcare edge AI.mp4

02:45

04 - Transport edge AI.mp4

03:12

05 - Manufacturing edge AI.mp4

02:08

04 - 3. Infrastructure for Edge AI

01 - Data for edge AI.mp4

02:33

02 - Processors for edge AI.mp4

02:26

03 - Devices and servers for edge AI.mp4

01:24

04 - Software infrastructure for edge AI.mp4

02:26

05 - Edge AI-specialized software.mp4

01:39

05 - 4. Training Models for Edge Deployments

01 - Building a model baseline.mp4

02:34

02 - Model compression.mp4

03:18

03 - Model optimization for the edge.mp4

02:24

04 - Testing models before deployments.mp4

02:16

05 - Federated learning.mp4

02:17

06 - 5. Edge AI Deployment and Operations

01 - Edge AI deployment architectures.mp4

04:21

02 - Deploying AI at the edge.mp4

02:30

03 - Model inference at the edge.mp4

01:32

04 - Edge AI data collection.mp4

01:51

05 - Publishing data from the edge.mp4

02:13

06 - Edge AI performance analysis.mp4

01:31

07 - 6. Best Practices for Building Edge AI Applications

01 - Infrastructure selection best practices.mp4

01:28

02 - Model selection and training best practices.mp4

01:46

03 - Model optimization best practices.mp4

01:59

04 - Model deployment best practices.mp4

01:35

05 - Data collection and analytics best practices.mp4

01:25

08 - Conclusion

01 - Building more with edge AI.mp4

00:33

Description

As edge devices like smartphones, cameras, and sensors become more powerful, the applications that manage them are moving from the cloud to edge. More and more AI models are now deployed on these devices in an attempt to reduce latency and secure greater levels of privacy. As a result, the AI community needs to familiarize itself with how to successfully build edge AI applications.

If you’re currently working on building AI applications in the enterprise or the cloud, or just looking to expand your existing skill set, this course is designed to help you get up to speed with exciting new developments in machine learning applications. Along the way, instructor Kumaran Ponnambalam covers a handful of real-world edge AI use cases drawn from industries such as retail, healthcare, transportation, manufacturing, and more.

Note: This course requires a basic working knowledge of machine learning processes, practices, and applications.

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Artificial Intelligence

Kumaran Ponnambalam

Instructor's Courses

A seasoned veteran in everything data, with a reputation for delivering high performance database and SaaS applications and currently specializing in leading Big Data Science and Engineering efforts

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