Emerging Technologies · PPL

Edge Computing Certification

A specialist program designed to develop practical skills in edge architecture, distributed computing, IoT integration, containers, data processing, security, orchestration, and cloud-edge environments.

  • ⏱3 DaysDuration
  • ◈PPLAccredited
  • ◐3 LanguagesArabic · English · Hindi
  • ◆₹14,999.00 Per delegate

This course is accredited by PPL

This is for all ppl accredited courses
2M+ Delegates trained worldwide
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4.8 ★ Average learner rating
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— The journey

Course Outline

What the programme covers, module by module.

Module 1: Introduction to Edge Computing

  • Edge computing fundamentals
  • Evolution of edge computing
  • Edge vs cloud computing
  • Distributed computing
  • Edge use cases
  • Industry applications

Module 2: Edge Computing Architecture

  • Edge architecture
  • Edge devices
  • Edge nodes
  • Gateways
  • Cloud layer
  • Data flow

Module 3: Edge Devices & Hardware

  • Edge devices
  • Embedded systems
  • Sensors
  • Industrial devices
  • Processing capabilities
  • Hardware considerations

Module 4: Edge Gateways

  • Gateway architecture
  • Device connectivity
  • Protocol conversion
  • Data aggregation
  • Local processing
  • Gateway management

Module 5: Edge Networking

  • Network fundamentals
  • LAN and WAN
  • Wireless connectivity
  • Network latency
  • Bandwidth
  • Connectivity management

Module 6: IoT & Edge Integration

  • IoT architecture
  • Sensors and devices
  • Device communication
  • IoT gateways
  • Data collection
  • Edge-IoT workflows

Module 7: Edge Communication Protocols

  • MQTT
  • HTTP
  • CoAP concepts
  • OPC UA concepts
  • Message communication
  • Protocol selection

Module 8: Edge Data Processing

  • Local data processing
  • Data filtering
  • Data aggregation
  • Stream processing
  • Event processing
  • Data transformation

Module 9: Real-Time Computing at the Edge

  • Real-time requirements
  • Low-latency processing
  • Event-driven systems
  • Local decision-making
  • Response time
  • Real-time applications

Module 10: Edge Analytics

  • Edge analytics concepts
  • Data analysis
  • Operational analytics
  • Anomaly detection
  • Pattern identification
  • Local intelligence

Module 11: Containers for Edge Computing

  • Container fundamentals
  • Container images
  • Docker concepts
  • Container deployment
  • Resource management
  • Edge containers

Module 12: Kubernetes at the Edge

  • Kubernetes fundamentals
  • Edge orchestration
  • Lightweight Kubernetes
  • Workload deployment
  • Cluster management
  • Edge nodes

Module 13: Edge Application Development

  • Edge application architecture
  • Microservices
  • APIs
  • Local services
  • Application packaging
  • Deployment workflows

Module 14: Cloud-Edge Integration

  • Hybrid architecture
  • Cloud connectivity
  • Data synchronisation
  • Workload distribution
  • Cloud services
  • Edge-to-cloud workflows

Module 15: Data Management at the Edge

  • Local storage
  • Data caching
  • Data lifecycle
  • Data synchronisation
  • Offline operations
  • Data retention

Module 16: AI & Machine Learning at the Edge

  • Edge AI concepts
  • ML inference
  • Model deployment
  • Computer vision applications
  • Intelligent devices
  • AI workload optimisation

Module 17: Edge Security

  • Edge security principles
  • Device security
  • Network security
  • Authentication
  • Encryption
  • Access control

Module 18: Edge Device Management

  • Device provisioning
  • Configuration
  • Remote management
  • Software updates
  • Firmware management
  • Device lifecycle

Module 19: Monitoring & Observability

  • Edge monitoring
  • Device health
  • Application metrics
  • Logs
  • Alerts
  • Performance monitoring

Module 20: Edge Reliability & Resilience

  • Fault tolerance
  • Offline operation
  • Connectivity failures
  • Recovery mechanisms
  • High availability
  • Resilient architecture

Module 21: Edge Performance Optimisation

  • Resource utilisation
  • Latency optimisation
  • Bandwidth optimisation
  • Application performance
  • Workload placement
  • Capacity considerations

Module 22: Integrated Edge Computing Project

  • Edge architecture design
  • Device connectivity
  • Local data processing
  • Container deployment
  • Cloud integration
  • Monitoring and optimisation
— 01.2 · Is it right for you?

Who it's for & what's included

Pick a delivery method to see exactly who it suits and everything you receive.

Who it's for

Classroom

Best for learners who want face-to-face tuition and to network with peers in person.

What's included

Everything you get

  • ✓ Live instructor on-site
  • ✓ Printed workbook & materials
  • ✓ Group exercises & case studies
Who it's for

Online Instructor-Led

Best for learners who want a live instructor and a fixed schedule, without the travel.

What's included

Everything you get

  • ✓ Live instructor via video call
  • ✓ Digital workbook & resources
  • ✓ Session recordings
Who it's for

Self-Paced

Best for self-motivated learners who need maximum flexibility around work and life.

What's included

Everything you get

  • ✓ On-demand video lessons
  • ✓ Interactive quizzes
  • ✓ 24/7 access on any device
— What you will master

Course Objectives

01

Understand edge computing architecture, components, and industry applications.

02

Design edge environments using devices, gateways, networking, and distributed resources.

03

Integrate IoT devices and communication protocols with edge platforms.

04

Implement real-time data processing and analytics closer to data sources.

05

Deploy containerised applications and orchestrate workloads at the edge.

06

Integrate edge infrastructure with cloud platforms and distributed services.

07

Integrate edge infrastructure with cloud platforms and distributed services.

08

Optimise edge workloads for latency, bandwidth, performance, and resource efficiency.

— Questions answered

Frequently Asked Questions

What is Edge Computing?
Edge Computing processes data closer to devices and data sources instead of relying entirely on centralised cloud infrastructure, helping reduce latency and bandwidth requirements.
Who should attend this course?
The course is suitable for cloud engineers, IoT engineers, DevOps professionals, software developers, infrastructure professionals, and technical working professionals interested in edge technologies.
Do I need previous cloud or IoT experience?
Basic knowledge of cloud computing, networking, IoT, Linux, or software development is helpful for understanding specialist-level edge computing concepts.
Which technologies and concepts are covered?
The course covers edge devices, gateways, MQTT, IoT integration, containers, Kubernetes, real-time processing, edge analytics, cloud-edge integration, security, monitoring, and Edge AI.
What practical skills will I develop?
You will develop skills in designing edge architectures, connecting devices, processing data locally, deploying containerised workloads, integrating cloud services, monitoring edge systems, and optimising performance.
— Trusted by learners

What our delegates say

★★★★★

"The structure, the practice exams, the instructor — all top tier. Passed first try."

AS
Aarti SharmaSenior Project Manager · TCS
★★★★★

"Best training I have attended. The content is exactly what modern projects need."

JD
James DonovanProgramme Director · Capgemini
★★★★★

"24/7 support actually means 24/7 — got help on my mock exam at 2am. Worth every dollar."

MO
Maya OkaforPMO Lead · Standard Bank

★ 4.8 / 5 from 12,000+ verified learner reviews on Trustpilot & Google.

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