Edge AI: Tools and Best Practices for Building AI Applications at the Edge
1h 19mIntermediate2024-01-09
Authors

Kumaran Ponnambalam
Working with data for 20+ years
Course details
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.
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.
Skills covered
Programming FoundationsCloud AdministrationArtificial Intelligence FoundationsArtificial Intelligence (AI)Cloud ComputingSoftware DevelopmentOne-Off
Concepts
0. Introduction
- 01 - Why AI at the edge
1. Edge Computing and Artificial Intelligence
- 02 - Internet of Things
- 03 - What is edge computing
- 04 - Benefits of edge computing
- 05 - Challenges of edge computing
- 06 - AI at the edge
- 07 - Benefits and challenges of edge AI
2. Edge AI Use Cases
- 08 - Personal edge AI
- 09 - Retail edge AI
- 10 - Healthcare edge AI
- 11 - Transport edge AI
- 12 - Manufacturing edge AI
3. Infrastructure for Edge AI
- 13 - Data for edge AI
- 14 - Processors for edge AI
- 15 - Devices and servers for edge AI
- 16 - Software infrastructure for edge AI
- 17 - Edge AI-specialized software
4. Training Models for Edge Deployments
- 18 - Building a model baseline
- 19 - Model compression
- 20 - Model optimization for the edge
- 21 - Testing models before deployments
- 22 - Federated learning
5. Edge AI Deployment and Operations
- 23 - Edge AI deployment architectures
- 24 - Deploying AI at the edge
- 25 - Model inference at the edge
- 26 - Edge AI data collection
- 27 - Publishing data from the edge
- 28 - Edge AI performance analysis
6. Best Practices for Building Edge AI Applications
- 29 - Infrastructure selection best practices
- 30 - Model selection and training best practices
- 31 - Model optimization best practices
- 32 - Model deployment best practices
- 33 - Data collection and analytics best practices
Conclusion
- 34 - Building more with edge AI
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