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

By leveraging machine learning and advanced data analytics, AI-driven predictive maintenance forecasts equipment failures and refines maintenance planning. This approach shifts industrial operations from reactive repairs to proactive management, resulting in improved system uptime, reduced costs, and extended asset lifespan.

This instructor-led training, available both online and on-site, targets intermediate professionals seeking to deploy AI-powered predictive maintenance within industrial settings.

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

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

Training Format

• Engaging lectures paired with interactive discussions.
• Practical exercises supported by real-world case studies.
• Live demonstrations featuring practical data processing workflows.

Customization Opportunities

• For tailored training options for this course, please reach out to us to arrange your needs.

AI-driven process optimization involves utilizing machine learning and data analytics to boost efficiency, quality, and throughput across manufacturing environments.

This instructor-led live training, available either online or onsite, is designed for manufacturing professionals with intermediate experience who aim to deploy AI strategies to streamline operations, minimize downtime, and foster continuous improvement.

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

• Grasp AI concepts specific to manufacturing optimization.
• Gather and preprocess production data for analytical purposes.
• Deploy machine learning models to detect bottlenecks and anticipate equipment failures.
• Visualize and analyze outcomes to facilitate data-informed decision-making.

Training Format

• Engaging lectures accompanied by interactive discussions.
• Numerous exercises and practical drills.
• Practical implementation within a live-lab setting.

Customization Options

• For tailored training sessions, please reach out to us to coordinate.

AI-driven Quality Control utilizes computer vision and machine learning algorithms to pinpoint defects, anomalies, and deviations within production workflows.

This instructor-led training, available both online and onsite, targets beginner to intermediate quality professionals seeking to deploy AI tools for automating inspections and elevating product quality in manufacturing settings.

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

• Grasping the application of AI in industrial quality control.
• Collecting and annotating image or sensor data from manufacturing lines.
• Employing machine learning and computer vision techniques to identify defects.
• Constructing basic AI models for anomaly detection and yield prediction.

Course Format

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

Customization Options

• For requests regarding customized training for this course, please contact us to coordinate.

Artificial Intelligence in Supply Chain and Manufacturing Logistics involves leveraging 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 aiming to apply AI-driven tools to improve logistics performance, accurately forecast demand, and automate warehouse and transport operations.

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

• Understand how AI is applied across logistics and supply chain activities.
• Use machine learning models for demand forecasting and inventory control.
• Analyze routes and optimize transport using AI-based techniques.
• Automate decision-making in warehouses and fulfillment processes.

Course Format

• 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 Smart Factories involves applying 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 who want a strategic and practical introduction to leveraging AI in smart factory environments.

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

• Understand the core 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.

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.

This workshop employs a project-based methodology to apply machine learning, computer vision, and data analytics for solving real-world industrial challenges using real or simulated datasets.

Led by an instructor, this live training (available online or on-site) targets intermediate-level cross-functional teams aiming to collaboratively implement AI solutions aligned with their operational objectives while gaining hands-on experience with industrial data pipelines.

Upon completion, participants will be equipped to:

• Identify and define practical AI applications in operations, quality assurance, or maintenance.
• Collaborate across various roles to build machine learning solutions.
• Manage, clean, and analyze diverse industrial datasets.
• Demonstrate a functional prototype of an AI-driven solution based on a chosen use case.

Training Format

• Interactive lectures and discussions.
• Collaborative exercises and project work in groups.
• Practical implementation within a live laboratory environment.

Customization Options

• To request a customized version of this course, please get in touch to arrange details.

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

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

Upon completing the training, participants will be able to:

Comprehend the fundamental concepts underlying intelligent bots.

Construct intelligent bots using cloud-based applications.

Acquire practical proficiency with 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 who are keen on bot development.

Course Format

The training blends 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, enabling faster task completion without requiring direct human intervention.

Through this instructor-led live training, participants will gain foundational knowledge in bot development by building sample chatbots using dedicated development tools and frameworks.

Upon completing this training, participants will be able to:

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

Audience

• Developers aiming to create their own bots

Course Format

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

Digital Twins serve as virtual simulations of physical entities, augmented by real-time information and artificial intelligence capabilities.

This guided, live training session (available online or on-site) targets mid-level professionals aiming to create, launch, and refine digital twin models by leveraging real-time data and AI-driven analytics.

Upon completion, participants will be capable of:

• Grasping the structure and key elements of digital twins.
• Employing simulation tools to represent complex systems and surroundings.
• Incorporating live data streams into virtual models.
• Utilizing AI methods for forecasting behaviors and identifying anomalies.

Course Format

• Engaging lectures and group discussions.
• Numerous practical exercises and drills.
• Practical application within a live lab setting.

Customization Opportunities

• For tailored training requirements, please reach out to us to coordinate.

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 aiming to deploy AI-driven logic and control systems in manufacturing settings where speed, reliability, and offline operation 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.
• Extensive exercises and practical practice.
• Hands-on implementation within a live-lab environment.

Course Customization Options

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

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

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

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

• Comprehending the architecture and key components of industrial vision systems.
• Constructing AI models for visual defect detection using deep learning techniques.
• Integrating real-time inspection pipelines with industrial cameras and hardware devices.
• Deploying and optimizing AI-powered inspection systems for production environments.

Course Format

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

Customization Options

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

A Smart Robot is an Artificial Intelligence (AI) system capable of learning from its environment and experiences, thereby enhancing its capabilities based on that accumulated knowledge. These intelligent systems can collaborate with humans, working alongside them and adapting by observing human behavior. Beyond performing manual labor, Smart Robots are also equipped to handle complex cognitive tasks. In addition to physical robotic bodies, Smart Robots can exist as purely software-based entities, residing within a computer as an application without any moving parts or direct physical 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. They will then apply this knowledge to complete their own Smart Robot projects.

The course is structured into 4 sections, each comprising three days of lectures, discussions, and hands-on robot development within a live lab environment. Each section concludes with a practical, hands-on project, allowing participants to practice and demonstrate the knowledge they have acquired.

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

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 in a robotic system
• Comprehend and implement the software components that support 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

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

Note

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