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

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

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

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

• Differentiate predictive maintenance from reactive and preventive maintenance strategies.
• Gather and organize machine data for AI-driven analysis.
• Utilize machine learning models to identify anomalies and forecast equipment failures.
• Deploy comprehensive workflows that transform sensor data into actionable insights.

Course Format

• Interactive lectures and discussions.
• Practical exercises and case studies.
• Live demonstrations and real-world data workflow practice.

Customization Options

• To request a customized version of this course, please contact us to arrange.

AI for Process Optimization involves applying machine learning and data analytics to improve efficiency, quality, and throughput in manufacturing operations.

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

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

• Understand AI concepts relevant to manufacturing optimization.
• Collect and prepare production data for analysis.
• Apply machine learning models to identify bottlenecks and predict failures.
• Visualize and interpret results to support data-driven decisions.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• 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 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 aim to apply AI tools to automate inspections and enhance product quality in manufacturing settings.

Upon completing this training, participants will be able to:

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

Format of the Course

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

Course Customization Options

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

AI in Supply Chain and Manufacturing Logistics leverages predictive analytics, machine learning, and automation to optimize inventory management, routing strategies, and demand forecasting.

This instructor-led live training, available online or onsite, targets intermediate-level supply chain professionals seeking to apply AI-driven tools to enhance logistics performance, improve demand forecasting accuracy, and automate warehouse and transport operations.

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

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

Course Format

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

Customization Options

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

Artificial intelligence in smart factories involves using AI 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 seeking a strategic and practical introduction to leveraging AI within smart factory environments.

Upon completing this training, participants will be able to:

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

Course Format

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

Course Customization Options

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

The implementation of AI use cases utilizes a hands-on, project-oriented methodology to apply machine learning, computer vision, and data analytics for addressing real-world industrial challenges through actual or simulated datasets.

This instructor-led, live training (available online or onsite) targets intermediate-level cross-functional teams seeking to collaboratively deploy AI solutions that align with their operational objectives and build experience working with industrial data pipelines.

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

• Identifying and defining practical AI use cases within operations, quality, or maintenance functions.
• Collaborating across various roles to create machine learning solutions.
• Managing, cleaning, and analyzing a variety of industrial datasets.
• Delivering a functional prototype of an AI-driven solution based on a chosen use case.

Course Format

• Interactive lectures and discussions.
• Group exercises and project-based work.
• Practical implementation within a live lab environment.

Customization Options for the Course

• For requests to customize this training, please contact us to arrange.

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

During this instructor-led live training, participants will learn how to efficiently develop intelligent bots using Microsoft Azure.

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

Grasp the fundamental concepts behind intelligent bots.

Construct intelligent bots using cloud-based applications.

Acquire practical knowledge of the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Apply established bot design patterns in real-world scenarios.

Create and deploy their first intelligent bot using Microsoft Azure.

Target Audience

This course is designed for developers, hobbyists, engineers, and IT professionals interested in bot development.

Course Format

The training combines lectures and discussions with exercises and a strong emphasis on hands-on practice.

A bot or chatbot is like a computer assistant that is used to automate user interactions on various messaging platforms and get things done faster without the need for users to speak to another human.

In this instructor-led, live training, participants will learn how to get started in developing a bot as they step 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 different uses and applications of bots
• Understand the complete process in developing bots
• Explore the different tools and platforms used in building bots
• Build a sample chatbot for Facebook Messenger
• Build 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 heavy hands-on practice

Digital Twins serve as virtual counterparts to physical systems, bolstered by live data feeds and artificial intelligence capabilities.

This guided, live training session (available online or in-person) targets intermediate professionals aiming to create, implement, and refine digital twin models leveraging real-time data and AI-derived insights.

Upon completing this training, participants will be equipped to:

• Grasp the structure and key elements of digital twins.
• Employ simulation software to model intricate systems and environments.
• Embed live data streams into virtual models.
• Utilize AI methods for forecasting behavior and identifying anomalies.

Course Format

• Engaging lectures and discussions.
• Abundant exercises and practical application.
• Practical implementation within a live laboratory setting.

Customization Options

• For tailored training on this topic, please reach out to us to coordinate.

Edge AI involves deploying artificial intelligence models directly onto devices and machines located at the edge of the network, facilitating 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-driven logic and control systems within manufacturing environments where speed, reliability, and offline operation are essential.

Upon completing 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 sessions.
• Hands-on implementation in a live laboratory environment.

Course Customization Options

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

AI-driven industrial computer vision is revolutionizing the way manufacturers and QA teams identify surface defects, verify part conformity, and automate visual inspection processes.

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

Upon completing this training, participants will be able to:

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

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• 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 represents an Artificial Intelligence (AI) system capable of learning from its surroundings and past experiences, thereby enhancing its capabilities through acquired knowledge. These robots can collaborate closely with humans, working alongside them while learning from human behavior. Beyond physical labor, they are equipped to handle cognitive tasks. In addition to hardware robots, Smart Robots can exist as purely software-based applications, running on a computer without physical components or direct interaction with the physical 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, applying this knowledge to complete their own Smart Robot projects.

The course is organized into 4 sections, each spanning three days of lectures, discussions, and hands-on robot development within a live lab environment. Each section concludes with a practical, hands-on project designed to allow participants to practice and demonstrate their newly acquired skills.

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

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

• Grasp the key concepts underlying robotic technologies.
• Understand and manage the interaction between software and hardware within a robotic system.
• Understand and implement the software components that form the foundation of Smart Robots.
• Build 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 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 heavy hands-on practice

Note

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