Real-Time Analytics in IIoT - Transforming Data Into Decisions

Overview

Explore real-time analytics in Industrial IoT, from architecture and communication layers to AI implementation, practical applications, and emerging trends in data-driven decision making.

Syllabus

• Introduction to Industrial IoT (IIoT)
Overview of IIoT and its significance
Key components and architecture of IIoT systems
• Real-Time Analytics Fundamentals
Definition and importance of real-time analytics
Comparison of real-time vs. batch processing
• Architecture of Real-Time Analytics in IIoT
Data collection and ingestion
Processing frameworks for real-time analytics
Edge vs. cloud computing in IIoT
• Communication Layers and Protocols
Overview of communication protocols used in IIoT
Importance of low-latency communication
• AI and Machine Learning in IIoT
Role of AI/ML in enhancing IIoT analytics
Overview of ML models for real-time analytics
AI-driven predictive maintenance
• Practical Applications of Real-Time Analytics in IIoT
Case studies of real-time analytics in manufacturing
Energy management and optimization
Quality control and production efficiency
• Data Management and Security in IIoT
Best practices for data management in IIoT systems
Understanding data privacy and security challenges
• Emerging Trends in Data-Driven Decision Making
Integration of IoT with AI and big data
Real-time decision-making platforms
Future advancements in IIoT analytics
• Capstone Project
Designing and implementing a real-time analytics solution for an IIoT use case
Presentation and evaluation of the project
• Review and Course Wrap-up
Key takeaways and lessons learned
Future learning paths and opportunities in IIoT analytics

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Subjects

Data Science