Course Details

IoT, AI, and Edge Computing in Smart Industries Training Course

Introduction

The modern industrial landscape is undergoing a profound transformation, driven by the convergence of three powerful technological forces: the Internet of Things (IoT), Artificial Intelligence (AI), and Edge Computing. Smart Industries, often synonymous with Industry 4.0, leverage this synergy to achieve unprecedented levels of automation, efficiency, predictive capabilities, and real-time decision-making. IoT devices, acting as the eyes and ears of these smart environments, collect vast amounts of data from machinery, sensors, and operational processes. AI, then, provides the intelligence to analyze this data, identify patterns, predict failures, optimize operations, and automate responses. However, sending all this raw data to centralized cloud servers for processing can introduce latency, consume excessive bandwidth, and raise privacy concerns, especially in critical industrial applications. This is where Edge Computing becomes indispensable. By processing data closer to its source – at the "edge" of the network, on IoT devices or local gateways – edge computing enables real-time insights, faster response times, reduced bandwidth usage, and enhanced data security. Without a comprehensive understanding of how these three technologies integrate, organizations risk fragmented solutions, inefficient operations, and an inability to realize the full potential of digital transformation in their industrial settings. Many businesses struggle with the complexity of deploying and managing these interconnected systems, facing challenges in data integration, model deployment on constrained devices, security across distributed networks, and ensuring interoperability. Conversely, mastering the integration of IoT, AI, and Edge Computing empowers professionals to design and implement truly intelligent, autonomous, and resilient industrial systems that drive significant competitive advantage, improve safety, and unlock new business models. Ignoring this critical convergence means falling behind in the race towards smarter, more agile, and highly optimized industrial operations. Our intensive 5-day "IoT, AI, and Edge Computing in Smart Industries" training course is meticulously designed to equip engineers, IT professionals, data scientists, solution architects, and business leaders with the essential knowledge and practical skills required to design, implement, and manage integrated IoT, AI, and Edge Computing solutions for various industrial applications.

This comprehensive program will delve into the core concepts of each technology and, more importantly, their synergistic integration. Participants will gain hands-on experience with practical applications, covering topics such as sensor data acquisition, edge device management, deploying lightweight AI models at the edge, real-time analytics, predictive maintenance, and addressing security and scalability challenges. By the end of this course, you will be proficient in conceptualizing, planning, and executing the deployment of intelligent edge solutions, empowering you to contribute effectively to advanced industrial automation, smart manufacturing, and other Industry 4.0 initiatives.

Duration

5 Days

Target Audience

The "IoT, AI, and Edge Computing in Smart Industries" training course is crucial for a wide range of technical and managerial professionals involved in modernizing industrial operations and leveraging advanced technologies. This includes:

• Industrial Automation Engineers: To integrate AI and Edge into existing control systems.
• IoT Developers and Engineers: Specializing in industrial applications and edge deployment.
• Data Scientists and Machine Learning Engineers: Focused on deploying models on edge devices.
• IT/OT Convergence Specialists: Bridging the gap between information technology and operational technology.
• Solution Architects: Designing robust and scalable smart industry solutions.
• Manufacturing Engineers and Managers: Seeking to optimize production and implement predictive maintenance.
• DevOps and MLOps Engineers: Responsible for deploying and managing AI models at the edge.
• Product Managers: Overseeing the development of smart industrial products and services.
• Cybersecurity Professionals: Addressing security challenges in distributed IoT and edge environments.
• Anyone involved in the digital transformation of industries interested in the practical application of these converging technologies.

Course Objectives

Upon successful completion of the "IoT, AI, and Edge Computing in Smart Industries" training course, participants will be able to:

• Understand the foundational concepts of IoT, AI, and Edge Computing and their individual roles in smart industries.
• Grasp the synergistic integration of these three technologies and their collective benefits for industrial applications.
• Design and implement scalable data acquisition strategies from various IoT devices for industrial use cases.
• Develop and optimize lightweight AI/ML models suitable for deployment on resource-constrained edge devices.
• Leverage edge computing platforms and frameworks for real-time data processing and local decision-making.
• Address key challenges related to security, data privacy, and network management in IoT-AI-Edge environments.
• Identify and apply real-world use cases for these integrated technologies in smart manufacturing, predictive maintenance, and operational optimization.
• Formulate a strategic approach for implementing and scaling IoT-AI-Edge solutions within an industrial setting.

 Course Modules

Module 1: Introduction to Smart Industries and the Technology Triad

• Defining Smart Industries and Industry 4.0: Concepts and drivers.
• Introduction to the Internet of Things (IoT): Components, architecture, and industrial IoT (IIoT).
• Overview of Artificial Intelligence (AI) and Machine Learning (ML) relevant to industrial applications.
• Understanding Edge Computing: Why it's crucial for IoT and AI in industrial settings.
• The convergence: How IoT generates data, Edge processes it locally, and AI provides intelligence.

Module 2: IoT Devices, Sensors, and Connectivity for Industrial Use

• Types of industrial IoT devices, sensors, and actuators (e.g., vibration, temperature, pressure, vision).
• IoT communication protocols: MQTT, CoAP, OPC UA, LoRaWAN, 5G.
• Data acquisition strategies: Real-time streaming vs. batch collection.
• Edge gateways: Role in data aggregation, filtering, and protocol translation.
• Managing and securing diverse IoT device fleets in an industrial environment.

Module 3: Edge Computing Platforms and Architectures

• Fundamentals of edge computing paradigms: From device edge to fog computing to micro data centers.
• Hardware considerations for edge devices: Microcontrollers, single-board computers, industrial PCs, AI accelerators.
• Software platforms for edge computing: AWS IoT Greengrass, Azure IoT Edge, Google Cloud IoT Edge (conceptual overview).
• Containerization (Docker) and orchestration (Kubernetes) at the edge for application deployment.
• Designing robust and resilient edge architectures for industrial operations.

Module 4: Deploying AI/ML Models at the Edge

• Overview of machine learning models suitable for edge inference (e.g., classification, anomaly detection).
• Model optimization techniques for resource-constrained devices: Quantization, pruning, distillation.
• Frameworks for edge AI deployment: TensorFlow Lite, ONNX Runtime, OpenVINO.
• Training models in the cloud and deploying to the edge (cloud-to-edge workflow).
• Over-the-air (OTA) updates and model lifecycle management at the edge.

Module 5: Real-time Data Processing and Analytics at the Edge

• Processing streaming data at the edge for immediate insights.
• Techniques for real-time anomaly detection and predictive analytics on edge devices.
• Implementing local data storage and caching mechanisms at the edge.
• Filtering and aggregating data at the source to reduce bandwidth and cloud processing costs.
• Edge analytics dashboards and visualization for operational insights.

Module 6: Industrial Use Cases: Smart Manufacturing & Predictive Maintenance

• Predictive Maintenance: Using IoT data and AI at the edge to forecast equipment failures and optimize maintenance schedules.
• Quality Control: Automated visual inspection with edge AI for real-time defect detection on production lines.
• Asset Tracking and Management: Real-time visibility of industrial assets and inventory.
• Process Optimization: Applying AI to sensor data for optimizing energy consumption and production efficiency.
• Worker Safety: Edge AI for real-time hazard detection and compliance monitoring.

Module 7: Security, Privacy, and Governance in IoT-AI-Edge

• Security challenges specific to IoT and edge environments (device vulnerabilities, network attacks).
• Implementing secure communication protocols and access controls for edge devices.
• Data privacy considerations for local processing and sensitive industrial data.
• Data governance strategies for distributed data generated at the edge.
• Compliance with industry regulations and standards for industrial data.

Module 8: Strategic Implementation & Future Trends

• Developing a strategic roadmap for adopting IoT, AI, and Edge Computing in your industrial organization.
• Cost-benefit analysis and ROI calculation for smart industry initiatives.
• Integration with existing enterprise systems (ERP, MES).
• Emerging trends: 5G for industrial IoT, Digital Twins, Federated Learning at the Edge, AI-driven robotics.
• Case studies of successful IoT-AI-Edge deployments in various industries.

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

• Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com