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

AI-powered predictive maintenance leverages machine learning and data analytics to predict equipment failures and optimize maintenance schedules. It transforms reactive maintenance practices into proactive strategies, enhancing uptime, reducing costs, and extending the life of assets.

This instructor-led, live training (available online or on-site) is designed for intermediate-level professionals looking to implement AI-driven predictive maintenance solutions in industrial settings.

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

• Understand the differences between predictive, reactive, and preventive maintenance strategies.
• Gather and organize machine data for AI analysis.
• Use machine learning models to identify anomalies and forecast failures.
• Implement comprehensive workflows from sensor data collection to actionable insights.

Format of the Course

• Interactive lectures and discussions.
• Hands-on exercises and case studies.
• Live demonstrations and practical data workflows.

Course Customization Options

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

AI for Process Optimization involves the use of machine learning and data analytics to enhance efficiency, quality, and throughput in manufacturing processes.

This instructor-led, live training (available online or on-site) is designed for intermediate-level manufacturing professionals who aim to apply AI techniques to streamline operations, minimize downtime, and support continuous improvement initiatives.

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

• Grasp AI concepts relevant to manufacturing optimization.
• Gather and prepare production data for analysis.
• Implement machine learning models to identify bottlenecks and predict failures.
• Visualize and interpret results to facilitate data-driven decisions.

Format of the Course

• Interactive lecture and discussion sessions.
• Extensive exercises and practical activities.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

AI for Quality Control involves the application of computer vision and machine learning methods to detect defects, anomalies, and deviations in manufacturing processes.

This instructor-led, live training (available both online and onsite) is designed for quality professionals at beginner to intermediate levels who aim to leverage AI tools for automating inspections and enhancing product quality in production environments.

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

• Gain an understanding of how AI is utilized in industrial quality control.
• Collect and label image or sensor data from manufacturing lines.
• Employ machine learning and computer vision techniques to identify defects.
• Develop simple AI models for anomaly detection and yield forecasting.

Format of the Course

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

Course Customization Options

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

AI in Supply Chain and Manufacturing Logistics involves the use of predictive analytics, machine learning, and automation to optimize inventory management, routing, and demand forecasting.

This instructor-led, live training (available online or onsite) is designed for intermediate-level supply chain professionals who wish to leverage AI-driven tools to enhance logistics performance, improve demand forecasting accuracy, and automate warehouse and transport operations.

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

• Understand how AI is utilized across various logistics and supply chain activities.
• Apply machine learning models for demand forecasting and inventory control.
• Analyze routes and optimize transportation using AI-driven techniques.
• Automate decision-making processes in warehouses and fulfillment centers.

Format of the Course

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

Course Customization Options

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

AI in Smart Factories involves the application of artificial intelligence to automate, monitor, and optimize industrial processes in real-time.

This instructor-led, live training (available online or on-site) is designed for beginner-level decision-makers and technical leads who want to gain a strategic and practical understanding of how AI can be utilized in smart factory settings.

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

• Grasp the fundamental principles of AI and machine learning.
• Recognize key applications of AI in manufacturing and automation.
• Examine how AI enhances predictive maintenance, quality control, and process optimization.
• Assess the steps required to initiate AI-driven projects.

Format of the Course

• Interactive lectures and discussions.
• Real-world case studies and group activities.
• Strategic frameworks and implementation guidance.

Course Customization Options

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

AI Use Case Implementation is a practical, project-oriented approach that applies 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 who aim to collaboratively implement AI use cases aligned with their operational goals and gain hands-on experience working with industrial data pipelines.

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

• Identify and define practical AI use cases from operations, quality, or maintenance.
• Collaborate effectively across different roles to develop machine learning solutions.
• Manage, clean, and analyze a variety of industrial datasets.
• Showcase a working prototype of an AI-enabled solution based on a chosen use case.

Format of the Course

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

Course Customization Options

• For customized training options for this course, please contact us to arrange.

The Azure Bot Service integrates the capabilities of the Microsoft Bot Framework and Azure Functions to facilitate the rapid development of intelligent bots.

In this instructor-led, live training, participants will learn how to efficiently create an intelligent bot using Microsoft Azure.

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

• Understand the basics of intelligent bots
• Learn how to develop intelligent bots using cloud applications
• Gain knowledge on utilizing the Microsoft Bot Framework, the Bot Builder SDK, and the Azure Bot Service
• Comprehend how to design bots using bot patterns
• Create their first intelligent bot using Microsoft Azure

Audience

• Developers
• Hobbyists
• Engineers
• IT Professionals

Format of the course

• Combination of lecture, discussion, practical exercises, and extensive hands-on practice

A chatbot or bot acts as a digital assistant that automates user interactions on various messaging platforms, enabling tasks to be completed quickly without the need for human-to-human communication.

In this instructor-led, live training, participants will learn how to start developing a bot by creating sample chatbots using bot development tools and frameworks.

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

• Understand the diverse uses and applications of bots
• Grasp the entire process of developing bots
• Explore the different tools and platforms used in building bots
• Create a sample chatbot for Facebook Messenger
• Create a sample chatbot using Microsoft Bot Framework

Audience

• Developers interested in creating their own bot

Format of the course

• Part lecture, part discussion, exercises, and extensive hands-on practice

Digital Twins are virtual replicas of physical systems enhanced with real-time data and AI-driven intelligence.

This instructor-led, live training (conducted online or on-site) is designed for intermediate-level professionals who aim to create, deploy, and optimize digital twin models using real-time data and AI-based insights.

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

• Comprehend the architecture and components of digital twins.
• Utilize simulation tools to model complex systems and environments.
• Integrate real-time data streams into virtual models.
• Apply AI techniques for predictive behavior and anomaly detection.

Format of the Course

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

Course Customization Options

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

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

This instructor-led, live training (available online or on-site) is designed for advanced-level embedded and IoT professionals who aim to implement AI-powered logic and control systems in manufacturing environments where speed, reliability, and offline operation are crucial.

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

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

Format of the Course

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

Course Customization Options

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

Industrial computer vision enhanced by AI is revolutionizing the way manufacturers and quality assurance (QA) teams identify surface defects, verify part compliance, and automate visual inspection processes.

This instructor-led, live training (available both online and on-site) is designed for intermediate to advanced QA teams, automation engineers, and developers who aim to design and implement computer vision systems for defect detection and inspection using AI techniques.

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

• Grasp the architecture and components of industrial vision systems.
• Develop AI models for visual defect detection using deep learning.
• Integrate real-time inspection pipelines with industrial cameras and devices.
• Deploy and optimize AI-driven inspection systems for production environments.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

A Smart Robot is an Artificial Intelligence (AI) system capable of learning from its environment and experiences, enhancing its abilities based on the acquired knowledge. These robots can work alongside humans, collaborating and learning from their actions. They are adept at both manual tasks and cognitive functions. Beyond physical robots, Smart Robots can also exist as software applications within a computer, with no physical components or interactions.

In this instructor-led, live training, participants will explore various technologies, frameworks, and techniques for programming mechanical Smart Robots. They will then apply this knowledge to complete their own Smart Robot projects.

The course is structured into four sections, each comprising three days of lectures, discussions, and hands-on robot development in a live lab setting. Each section concludes with a practical, hands-on project to reinforce the participants' newly acquired skills.

The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, along with C++ and Python, will be used for programming the robots.

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

• Comprehend the fundamental concepts of robotic technologies
• Understand and manage the interaction between software and hardware in a robotic system
• Implement the software components essential for Smart Robots
• Create and operate a simulated mechanical Smart Robot that can see, sense, process, grasp, navigate, and interact with humans through voice commands
• Enhance a Smart Robot's capabilities to perform complex tasks using Deep Learning
• Test and troubleshoot a Smart Robot in realistic scenarios

Audience

• Developers
• Engineers

Format of the course

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

Note

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