Smart Robotics in Manufacturing: AI for Perception, Planning, and Control Training Course

AI-driven predictive maintenance leverages machine learning and advanced data analytics to anticipate equipment failures and refine maintenance schedules. This approach shifts organizations from reactive maintenance models to proactive strategies, resulting in improved operational uptime, reduced costs, and extended asset life.

This instructor-led training session, available either online or on-site, is designed for intermediate-level professionals seeking to deploy AI-based predictive maintenance solutions within industrial settings.

Upon completion of this course, participants will be equipped to:

• Distinguish predictive maintenance from reactive and preventive maintenance strategies.
• Gather and organize machine-generated data for AI-driven analysis.
• Utilize machine learning models to identify anomalies and forecast potential failures.
• Establish end-to-end workflows that transform raw sensor data into actionable insights.

Course Format

• Interactive lectures and group discussions.
• Practical exercises and real-world case studies.
• Live demonstrations and hands-on data processing workflows.

Customization Options

• For tailored training requirements, please contact us to arrange a customized program.

AI for Process Optimization involves leveraging machine learning and data analytics to boost efficiency, product quality, and throughput within manufacturing environments.

This instructor-led live training, available either online or at your location, is designed for intermediate-level manufacturing professionals seeking to apply AI techniques to streamline operations, minimize downtime, and drive continuous improvement initiatives.

Upon completion of this training, participants will be capable of:

• Grasping AI concepts pertinent to manufacturing optimization.
• Gathering and preparing production data for in-depth analysis.
• Utilizing machine learning models to pinpoint bottlenecks and forecast equipment failures.
• Visualizing and interpreting results to facilitate data-driven decision-making.

Course Format

• Interactive lectures and group discussions.
• Numerous exercises and practical practice sessions.
• Hands-on implementation within a live laboratory environment.

Customization Options

• To arrange a customized training session for this course, please get in touch with us.

AI-driven Quality Control leverages computer vision and machine learning techniques to identify defects, anomalies, and deviations within production processes.

This instructor-led, live training (available online or onsite) is designed for beginner to intermediate-level quality professionals who wish to apply AI tools to automate inspections and enhance product quality in manufacturing environments.

Upon completion of this training, participants will be able to:

• Understand the application of AI in industrial quality control.
• Collect and label image or sensor data from production lines.
• Utilize machine learning and computer vision to detect defects.
• Develop simple AI models for anomaly detection and yield forecasting.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request customized training for this course, please contact us to arrange.

Artificial Intelligence in Supply Chain and Manufacturing Logistics involves utilizing predictive analytics, machine learning, and automation to optimize inventory management, routing strategies, and demand forecasting.

This instructor-led training session (available online or onsite) is designed for intermediate-level supply chain professionals seeking to leverage AI-driven tools to enhance logistics performance, achieve precise demand forecasts, and automate warehouse and transport operations.

Upon completion of this training, participants will be able to:

• Comprehend the application of AI across various logistics and supply chain activities.
• Utilize machine learning models for accurate demand forecasting and inventory control.
• Analyze and optimize transport routes using AI-based techniques.
• Automate decision-making processes within warehouses and fulfillment workflows.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• To request a tailored training program for this course, please contact us to arrange.

AI in Smart Factories refers to the deployment of artificial intelligence to automate, monitor, and optimize industrial operations in real time.

This instructor-led, live training (available online or onsite) is designed for beginner-level decision-makers and technical leads seeking a strategic and practical introduction to leveraging AI within smart factory environments.

Upon completion of this training, participants will be able to:

• Grasp the fundamental principles of AI and machine learning.
• Recognize primary AI use cases in manufacturing and automation.
• Examine how AI facilitates predictive maintenance, quality control, and process optimization.
• Assess the stages involved in initiating AI-driven projects.

Course Format

• Interactive lectures and discussions.
• Real-world case studies and collaborative exercises.
• Strategic frameworks and implementation guidance.

Customization Options

• To request customized training for this course, please contact us to arrange.

AI Use Case Implementation offers a hands-on, project-based methodology for applying machine learning, computer vision, and data analytics to address real-world industrial challenges using actual or simulated datasets.

This instructor-led, live training (available online or onsite) is designed for intermediate-level cross-functional teams seeking to collaboratively implement AI use cases aligned with their operational goals and gain practical experience with industrial data pipelines.

Upon completion of this training, participants will be able to:

• Identify and scope practical AI use cases from operations, quality, or maintenance.
• Collaborate across roles to develop machine learning solutions.
• Process, clean, and analyze diverse industrial datasets.
• Present a functional prototype of an AI-enabled solution based on a selected use case.

Course Format

• Interactive lectures and discussions.
• Group-based exercises and project work.
• Hands-on implementation in a live-lab environment.

Customization Options

• To request customized training for this course, please contact us to arrange.

Azure Bot Service unites the capabilities of the Microsoft Bot Framework with Azure Functions, offering a robust platform for rapidly constructing intelligent bots.

Through this instructor-led live training, participants will investigate efficient methods for developing intelligent bots utilizing Microsoft Azure.

Upon completing the training, participants will be able to:

Comprehend the fundamental concepts underpinning intelligent bots.

Construct intelligent bots using cloud-based applications.

Acquire practical expertise in the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Implement established bot design patterns in practical scenarios.

Create and deploy their first intelligent bot using Microsoft Azure.

Target Audience

This course is tailored for developers, hobbyists, engineers, and IT professionals keen on bot development.

Course Format

The training integrates lectures and discussions with exercises, placing a strong emphasis on practical, hands-on application.

A bot or chatbot functions as a digital assistant designed to automate user interactions across various messaging platforms, enabling faster task completion without requiring direct human intervention.

Through this instructor-led live training, participants will learn how to begin developing bots by building sample chatbots using dedicated bot development tools and frameworks.

By the conclusion of this training, participants will be able to:

• Understand the various uses and applications of bots
• Comprehend the complete bot development process
• Explore the different tools and platforms utilized in bot construction
• Construct a sample chatbot for Facebook Messenger
• Construct a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bot

Format of the course

• A mix of lectures, discussions, exercises, and extensive hands-on practice

Digital Twins serve as virtual representations of physical entities, augmented by live data streams and artificial intelligence.

This instructor-led live training, available either online or in-person, targets intermediate-level professionals looking to create, deploy, and refine digital twin models utilizing real-time information and AI-derived insights.

Upon completion of this training, participants will gain the ability to:

• Grasp the architecture and key components of digital twins.
• Utilise simulation tools to model intricate systems and environments.
• Incorporate live data streams into virtual models.
• Apply AI methodologies for predictive analysis and anomaly identification.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical application.
• Hands-on implementation within a live laboratory setting.

Customisation Options

• To request tailored training for this course, please get in touch with us to organise.

Edge AI involves deploying artificial intelligence models directly onto devices and machinery at the network's edge, facilitating real-time decision-making with minimal latency.

This instructor-led, live training (available online or onsite) is designed for advanced-level embedded and IoT professionals aiming to deploy AI-driven logic and control systems in manufacturing settings where speed, reliability, and offline capability are paramount.

Upon completion of this training, participants will be able to:

• Grasp the architecture and advantages of edge AI systems.
• Construct and optimize AI models for deployment on embedded devices.
• Utilize tools such as TensorFlow Lite and OpenVINO for low-latency inference.
• Integrate edge intelligence with sensors, actuators, and industrial protocols.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical activities.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• To request customized training for this course, please contact us to arrange.

Artificial intelligence is revolutionizing industrial computer vision, enabling manufacturers and quality assurance teams to detect surface defects, verify part compliance, and automate visual inspection workflows.

This instructor-led live training, available either online or on-site, targets intermediate to advanced quality assurance teams, automation engineers, and developers. The course focuses on designing and implementing computer vision systems for defect detection and inspection using AI techniques.

Upon completion, participants will be capable of:

• Grasping the architecture and core components of industrial vision systems.
• Developing AI models for visual defect detection utilizing deep learning.
• Integrating real-time inspection pipelines with industrial cameras and hardware.
• Deploying and optimizing AI-driven inspection systems within production environments.

Course Format

• Interactive lectures and group discussions.
• Extensive exercises and practical practice.
• Hands-on implementation in a live-lab setting.

Customization Options

• For customized training requests, please contact us to make arrangements.

A Smart Robot represents an Artificial Intelligence (AI) system capable of learning from its surroundings and past experiences, thereby enhancing its capabilities based on that acquired knowledge. These intelligent entities can collaborate with humans, working alongside them and observing their behavior to adapt. Beyond performing manual labor, Smart Robots are equipped to handle complex cognitive tasks. It is important to note that Smart Robots are not limited to physical hardware; they can also exist purely as software applications within a computer, operating without moving parts or direct physical interaction with the world.

In this instructor-led, live training, participants will explore the various technologies, frameworks, and techniques required to program different types of mechanical Smart Robots. The course culminates in participants applying this knowledge to complete their own Smart Robot projects.

The curriculum is structured into 4 distinct sections. Each section spans three days, featuring lectures, interactive discussions, and hands-on robot development within a live laboratory environment. To ensure practical mastery, each section concludes with a practical, hands-on project, allowing participants to practice and demonstrate their newly acquired skills.

The hardware targeted in this course will be simulated in 3D using specialized simulation software. Programming for the robots will utilize the open-source ROS (Robot Operating System) framework, along with C++ and Python.

Upon completion of this training, participants will be able to:

• Grasp the core concepts underpinning robotic technologies
• Understand and manage the interaction between software and hardware within a robotic system
• Comprehend and implement the software components that form the foundation of Smart Robots
• Construct and operate a simulated mechanical Smart Robot capable of seeing, sensing, processing, grasping, navigating, and interacting with humans via voice
• Enhance a Smart Robot's capacity to execute complex tasks through the application of Deep Learning
• Test and troubleshoot a Smart Robot within realistic scenarios

Audience

• Developers
• Engineers

Format of the course

• A blend of lectures, discussions, exercises, and intensive hands-on practice

Note

• To customize any aspect of this course (such as the programming language or robot model), please contact us to make arrangements.