Real World AI - Tackling Dynamic MLOps Challenges for Edge Devices

Overview

Join us for an insightful exploration of how integrated platforms address the unique challenges associated with deploying Edge AI across remote, cellular-connected devices. This event sheds light on the importance of data integration, seamless connectivity, and efficient over-the-air software delivery in tackling dynamic MLOps challenges.

Hosted by leading experts in the field, this course offers valuable perspectives for anyone interested in the growing field of Artificial Intelligence and its application in real-world scenarios.

Whether you're a student or a professional, gain the knowledge needed to excel in this evolving landscape.

Syllabus

• Introduction to Edge AI
Overview of Edge Computing and AI
Benefits and Challenges of Edge AI
• MLOps Fundamentals
Introduction to MLOps Concepts
The MLOps Lifecycle
• Integrated Platforms for Edge AI
Data Integration and Management
Connectivity Solutions for Remote and Cellular Devices
• Data Pipeline Design for Edge Devices
Data Collection and Preprocessing Techniques
Real-Time Data Management Strategies
• Over-the-Air (OTA) Software Delivery
Principles of OTA Updates for Edge Devices
Ensuring Security and Reliability in Software Deployment
• Edge AI Model Deployment
Architectures for Edge AI Models
Tools and Frameworks for Edge Model Management
• Monitoring and Maintaining Edge AI Systems
Performance Metrics and Monitoring Techniques
Troubleshooting and Incident Management
• Security and Compliance in Edge AI
Addressing Security Risks in Edge Deployments
Legal and Regulatory Considerations
• Case Studies and Real-World Applications
Industry-Specific Examples of Edge AI
Lessons Learned from Successful Deployments
• Hands-on Labs and Projects
Building a Simple Edge AI Application
Over-the-Air Deployment Simulation
• Final Assessment and Course Wrap-Up
Review of Key Concepts
Future Trends in Edge AI and MLOps

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Subjects

Computer Science