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 organizations from reactive maintenance models to proactive strategies, resulting in improved uptime, reduced costs, and extended asset lifespan.

This instructor-led training, available online or onsite, is designed for intermediate-level professionals seeking to implement AI-driven predictive maintenance solutions within industrial environments.

Upon completing this training, participants will be able to:

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

Course Format

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

Customization Options

• To request tailored training for this course, please contact us to make arrangements.

Leveraging AI for Process Optimization involves utilizing machine learning and data analytics to boost efficiency, enhance quality, and increase throughput in manufacturing settings.

This instructor-led live training, available online or onsite, is designed for intermediate-level manufacturing professionals seeking to apply AI techniques to streamline operations, minimize downtime, and foster continuous improvement.

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

• Grasp AI concepts pertinent to manufacturing optimization.
• Gather and prepare production data for analysis.
• Deploy machine learning models to pinpoint bottlenecks and forecast failures.
• Visualize and interpret results to facilitate data-driven decision-making.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Practical implementation within a live-lab environment.

Course Customization Options

• To request customized training for this course, please reach out to us to make arrangements.

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

This instructor-led live training (available online or onsite) targets beginner to intermediate quality professionals who aim to leverage AI tools to automate inspections and enhance product quality in manufacturing environments.

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

• Understand how AI is applied 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.
• 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.

Leveraging AI in supply chain and manufacturing logistics involves applying predictive analytics, machine learning, and automation to enhance inventory management, optimize routing, and improve demand forecasting.

This instructor-led live training, available online or onsite, is designed for intermediate-level supply chain professionals seeking to utilize AI-driven tools to boost logistics performance, achieve accurate demand forecasting, and automate warehouse and transportation operations.

Upon completing this training, participants will be able to:

• Gain insights into how AI is utilized across logistics and supply chain activities.
• Apply machine learning models to forecast demand and manage inventory.
• Analyze and optimize transport routes using AI-based techniques.
• Automate decision-making processes in warehouses and fulfillment operations.

Course Format

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

Course Customization Options

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

The application of artificial intelligence in smart factories involves automating, monitoring, and optimizing 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:

• Comprehend the fundamental principles of AI and machine learning.
• Identify key AI use cases in manufacturing and automation.
• Explore how AI supports predictive maintenance, quality control, and process optimization.
• Evaluate the steps involved in launching 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.

This AI Use Case Implementation workshop adopts a project-driven, hands-on methodology to apply machine learning, computer vision, and data analytics for resolving real-world industrial challenges, utilizing actual or simulated datasets.

Delivered as an instructor-led, live training session (available online or onsite), this program targets intermediate-level cross-functional teams aiming to collaboratively execute AI use cases aligned with their operational objectives while gaining practical experience with industrial data pipelines.

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

• Identify and define practical AI use cases within operations, quality control, or maintenance.
• Collaborate across different roles to develop machine learning solutions.
• Manage, 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 within a live laboratory environment.

Course Customization Options

• To request customized training for this course, 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.

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

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

Comprehend the fundamental concepts underlying intelligent bots.

Construct intelligent bots utilizing 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 real-world scenarios.

Create and deploy their first intelligent bot using Microsoft Azure.

Audience

This course is tailored for developers, hobbyists, engineers, and IT professionals with an interest in bot development.

Course Format

The training merges lectures and discussions with exercises, placing a strong emphasis on hands-on practice.

A bot, or chatbot, functions as a digital assistant designed to automate user interactions across various messaging platforms. It enables faster task completion without requiring direct contact with human agents.

In this instructor-led live training, participants will learn how to begin developing bots by stepping through the creation of sample chatbots using dedicated development tools and frameworks.

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

• Grasp the diverse uses and applications of bots
• Understand the entire bot development lifecycle
• Explore various tools and platforms used for bot construction
• Construct a sample chatbot for Facebook Messenger
• Develop a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bots

Course Format

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

Digital Twins serve as virtual replicas of physical systems, augmented by real-time data and AI-driven intelligence.

This instructor-led live training, available either online or onsite, targets intermediate-level professionals eager to construct, deploy, and optimize digital twin models leveraging real-time data and AI insights.

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

• Comprehending the architecture and components of digital twins.
• Employing simulation tools to model intricate systems and environments.
• Integrating real-time data streams into virtual models.
• Applying AI methodologies for predictive behavior analysis and anomaly detection.

Course Format

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

Course Customization Options

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

Edge AI involves deploying artificial intelligence models directly onto devices and machines 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 seeking to deploy AI-driven logic and control systems in manufacturing environments where speed, reliability, and offline operation are paramount.

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

• Grasping the architecture and advantages of edge AI systems.
• Constructing and optimizing AI models for deployment on embedded devices.
• Utilizing tools such as TensorFlow Lite and OpenVINO for low-latency inference.
• Integrating edge intelligence with sensors, actuators, and industrial protocols.

Course Format

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

Course Customization Options

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

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

This instructor-led, live training (available online or onsite) targets intermediate to advanced QA teams, automation engineers, and developers aiming to design and deploy computer vision systems for defect detection and inspection using AI methodologies.

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

• Gaining a comprehensive understanding of industrial vision system architecture and components.
• Developing AI models for visual defect detection utilizing deep learning techniques.
• Integrating real-time inspection pipelines with industrial cameras and devices.
• Deploying and optimizing AI-powered inspection systems within production environments.

Course Format

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

Course Customization Options

• For customized training requests 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 through accumulated knowledge. These robots can collaborate with humans, working alongside them and learning from their behavior. Beyond performing manual labor, they are equipped to handle cognitive tasks as well. In addition to physical robots, Smart Robots can also exist purely as software applications on a computer, without any moving parts or physical interaction with the world.

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

The course is divided into 4 sections, each consisting of three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section concludes 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, along with 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
• Understand and manage the interaction between software and hardware in a robotic system
• Understand and implement the software components that underpin Smart Robots
• Build and operate a simulated mechanical Smart Robot that can see, sense, process, grasp, navigate, and interact 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 heavy hands-on practice

Note

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