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 models into proactive strategies, enhancing uptime, reducing costs, and extending the lifespan of assets.

This instructor-led, live training (online or onsite) is designed for intermediate-level professionals who wish to implement AI-driven predictive maintenance solutions in industrial environments.

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

• Understand how predictive maintenance differs from reactive and preventive maintenance approaches.
• Gather and structure machine data for AI-powered analysis.
• Apply machine learning models to identify anomalies and predict failures.
• Implement comprehensive workflows from sensor data to actionable insights.

Format of the Course

• Interactive lecture and discussion.
• Practical exercises and case studies.
• Live demonstration 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 pertinent 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.
• 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.

AI for Quality Control involves the use of computer vision and machine learning techniques to identify defects, anomalies, and deviations in production processes.

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

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

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

Format of the Course

• Interactive lecture and discussion.
• 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.

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 want to leverage AI-driven tools to improve logistics performance, achieve accurate demand forecasting, 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 management.
• Analyze and optimize transport routes using AI-based techniques.
• Automate decision-making processes in warehouses and fulfillment centers.

Format of the Course

• Interactive lectures and discussions.
• Numerous 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 in Smart Factories involves the application of artificial intelligence to automate, monitor, and optimize industrial operations 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 environments.

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

• Grasp the fundamental principles of AI and machine learning.
• Recognize key AI applications in manufacturing and automation.
• Explore how AI can support predictive maintenance, quality control, and process optimization.
• Assess the steps required to launch AI-driven initiatives.

Format of the Course

• Interactive lecture and discussion.
• Real-world case studies and group exercises.
• Strategic frameworks and implementation guidance.

Course Customization Options

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

AI Use Case Implementation is a practical, project-focused approach to 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 on-site) is designed for intermediate-level cross-functional teams who want to collaboratively implement AI use cases that align 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 areas.
• Collaborate effectively across different roles to develop machine learning solutions.
• Manage, clean, and analyze a variety of industrial datasets.
• Present a functional 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

• To request a customized training 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 insight into utilizing the Microsoft Bot Framework, the Bot Builder SDK, and the Azure Bot Service
• Discover 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

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

A bot or chatbot acts like a digital assistant that automates user interactions on various messaging platforms, allowing tasks to be completed quickly without the need for users to engage with another person.

In this instructor-led, live training, participants will learn how to start developing a bot as they work through the creation of 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 involved in developing bots
• Explore the different tools and platforms used for building bots
• Create a sample chatbot for Facebook Messenger
• Develop 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 by real-time data and AI-driven intelligence.

This instructor-led, live training (online or onsite) is designed for intermediate-level professionals who aim to build, 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:

• Understand 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 lecture and discussion.
• 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 at the network's edge, enabling real-time decision-making with minimal delay.

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

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

• Grasp the architecture and advantages of edge AI systems.
• Develop 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.

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.

Industrial computer vision with AI is revolutionizing the way manufacturers and QA teams identify surface defects, verify part compliance, and automate visual inspection processes.

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced-level QA teams, automation engineers, and developers who are looking 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 workflows with industrial cameras and devices.
• Deploy and optimize AI-driven inspection systems for production environments.

Format of the Course

• Interactive lecture and discussion.
• 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.

A Smart Robot is an Artificial Intelligence (AI) system capable of learning from its environment and experiences, enhancing its capabilities based on that knowledge. These robots can collaborate with humans, working alongside them and learning from their behavior. They are not limited to manual labor but can also perform cognitive tasks. In addition to physical robots, Smart Robots can be purely software-based, residing in a computer as a software application without any moving parts or physical interaction with the world.

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

The course is divided into four sections, each consisting of three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section will conclude with a practical hands-on project to allow participants to practice and demonstrate their acquired knowledge.

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

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

• Understand the key concepts used in robotic technologies
• Manage the interaction between software and hardware in a robotic system
• Implement the software components that underpin Smart Robots
• Build and operate a simulated mechanical Smart Robot capable of seeing, sensing, processing, grasping, navigating, and interacting with humans through voice
• Extend a Smart Robot's ability to perform complex tasks through Deep Learning
• Test and troubleshoot a Smart Robot in realistic scenarios

Audience

• Developers
• Engineers

Format of the course

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

Note

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