Edge Computing Certified Course

Course Description:

The Edge Computing Certified Course is a specialized program designed to equip learners with the foundational and advanced concepts of edge computing. This course provides a comprehensive understanding of how data processing can be moved closer to the source of data generation, reducing latency, improving real-time responsiveness, and optimizing bandwidth usage. Participants will explore the architecture, components, and applications of edge computing across various domains including IoT, smart cities, autonomous systems, healthcare, and industrial automation.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After Edge Computing Certified Course:

• Edge Computing Engineer
• IoT Solutions Architect
• Embedded Systems Developer
• Cloud and Edge Integration Specialist
• AI/ML Engineer (Edge Deployment)
• Network Systems Engineer
• Edge Security Analyst
• DevOps Engineer for Edge Infrastructure
• Smart Systems
• Edge Data Analyst

Essential Skills you will Develop Edge Computing Certified Course:

• Fundamentals of Edge Computing
• IoT Device Management
• Microservices and Containerization
• Edge AI & ML Deployment

Tools Covered:

• Dicker 
• Cabernets 
• Node-RED
• Tensor Flow Late / Opening 
• Anisole / Puppet 

Syllabus:

Module 1: Introduction to Edge Computing Evolution from Cloud to Edge Computing Key Concepts: Latency, Bandwidth, Real-Time Processing Edge vs. Cloud vs. Fog Computing Use Cases in Various Industries.

Module 2: Edge Architecture and Components Edge Nodes, Gateways, and Edge Servers Communication Protocols (MQTT, Copal, HTTP) Device-to-Device and Device-to-Cloud Models Introduction to Edge Hardware (Raspberry Pi, Jetson Nano).

Module 3: IoT and Edge Integration IoT Ecosystem Overview IoT Device Configuration and Management Data Acquisition and Transmission Protocols and Interfacing with Edge Gateways.

Module 4: Edge Application Development Programming for Edge Devices (Python, C++) Real-Time Application Design Using Node-RED for Edge Flow Programming Microservices at the Edge.

Module 5: Edge AI and Machine Learning AI on Edge Devices – Concepts and Challenges Deploying Lightweight ML Models (Tens or Flow Lite, Open VINO) Real-time Image and Sensor Data Processing Case Study: Smart Surveillance System.

Module 6: Containerization and Orchestration Introduction to Docker and Containers Deploying Edge Applications with Docker Using K3s / MicroK8s for Edge Kubernetes Hands-on: Building and Deploying Edge Containers.

Module 7: Edge-to-Cloud Integration Cloud Platforms (AWS Greengrass, Azure IoT Edge, Google Edge TPU) Syncing and Managing Data Across Edge and Cloud Data Filtering and Local Decision Making Edge API Gateways and Data Pipelines.

Module 8: Security and Privacy in Edge Computing Common Threats in Edge Environments Edge Device Authentication and Encryption Secure Communication (TLS, OpenSSL) Privacy Compliance (GDPR, HIPAA).

Module 9: Performance Optimization and Monitoring Network Configuration for Low Latency Resource Optimization (CPU, Memory, Power) Monitoring Tools (Graafian, Prometheus for Edge) Troubleshooting Edge Networks and Applications.

Module 10: Capstone Project and Industry Application Design and Deployment of a Full Edge Solution Use Cases: Smart Cities, Industry 4.0, Healthcare, Retail Project Presentation and Peer Review Future Trends in Edge, 5G, and AI Integration.

Industry Projects:

• Smart Traffic Monitoring System
• Edge-Based Predictive
• Retail Store Smart

Who is this program for?

• Software Developers
• Cloud & DevOps Professionals
• AI/ML Engineers

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com