Edge AI for Manufacturing: Real-Time Intelligence at the Device Level Training Course

This instructor-led, live training in Brazil (online or in person) targets intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring how 5G networks speed up Edge AI applications.

Upon completing this training, participants will be able to:

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models optimized for low-latency applications in 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimize AI workloads for efficient performance on edge devices.

6G and the Intelligent Edge is a forward-looking course that explores the integration of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to support intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT architects who wish to understand and design next-generation distributed architectures leveraging the synergy of 6G connectivity and intelligent edge systems.

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

• Understand how 6G will transform edge computing and IoT architectures.
• Design distributed systems for ultra-low latency, high bandwidth, and autonomous operations.
• Integrate AI and data analytics at the edge for intelligent decision-making.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures.
• Evaluate business and operational models enabled by 6G-edge convergence.

Format of the Course

• Interactive lectures and discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation with optional edge or container tools.

Course Customization Options

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

This instructor-led, live training in Brazil (online or onsite) is designed for advanced-level AI practitioners, researchers, and developers who aim to master the latest Edge AI advancements, optimize their models for edge deployment, and explore specialized applications across various industries.

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

• Explore advanced techniques for developing and optimizing Edge AI models.
• Implement leading-edge strategies for deploying AI models on edge devices.
• Utilize specialized tools and frameworks for advanced Edge AI applications.
• Enhance the performance and efficiency of Edge AI solutions.
• Investigate innovative use cases and emerging trends in Edge AI.
• Navigate advanced ethical and security considerations in Edge AI deployments.

This instructor-led, live training in Brazil (online or in-person) is designed for intermediate-level developers, data scientists, and technology enthusiasts who want to acquire practical skills for deploying AI models on edge devices for various applications.

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

• Grasping the core principles of Edge AI and its advantages.
• Configuring and setting up the edge computing environment.
• Creating, training, and refining AI models for edge deployment.
• Executing practical AI solutions on edge devices.
• Assessing and enhancing the performance of models deployed on the edge.
• Tackling ethical and security issues related to Edge AI applications.

Predictive maintenance for industrial systems, powered by artificial intelligence, utilizes machine learning and data analytics to anticipate equipment failures and refine maintenance schedules. This approach shifts organizations from reactive maintenance models to proactive strategies, resulting in improved operational uptime, reduced costs, and extended asset lifespan.

This instructor-led live training, available either online or on-site, is designed for intermediate-level professionals seeking to deploy AI-driven predictive maintenance solutions within industrial settings.

Upon completing this training, participants will be capable of:

• Distinguishing predictive maintenance from reactive and preventive maintenance strategies.
• Collecting and organizing machine data for artificial intelligence analysis.
• Applying machine learning models to identify anomalies and forecast failures.
• Implementing comprehensive workflows that transform sensor data into actionable insights.

Course Format

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

Options for Customizing the Course

AI for Process Optimization involves utilizing machine learning and data analytics to boost efficiency, quality, and throughput within manufacturing operations.

This instructor-led training (available online or onsite) is designed for intermediate-level manufacturing professionals looking to leverage AI techniques to streamline operations, minimize downtime, and drive continuous improvement.

By the conclusion of this training, participants will be capable of:

• Grasping AI concepts pertinent to manufacturing optimization.
• Collecting and preparing production data for analysis.
• Implementing machine learning models to identify bottlenecks and forecast failures.
• Visualizing and interpreting results to facilitate data-driven decision-making.

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

AI for Quality Control involves utilizing computer vision and machine learning methods to identify defects, anomalies, and deviations within production processes.

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

Upon completing this training, participants will be capable of:

• Understanding the application of AI in industrial quality control.
• Collecting and labeling image or sensor data from production lines.
• Leveraging machine learning and computer vision to detect defects.
• Developing basic AI models for anomaly detection and yield forecasting.

Course Format

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

Course Customization Options

• To request 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 streamline inventory management, optimize routing, and improve demand forecasting.

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

Upon completing this course, participants will be equipped to:

• Grasp how AI is integrated into logistics and supply chain workflows.
• Implement machine learning models for demand forecasting and inventory management.
• Analyze and optimize transport routes using AI techniques.
• Automate decision-making processes within warehouses and fulfillment centers.

Course Format

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

Customization Options

• For customized training options, please reach out to us to make arrangements.

Artificial Intelligence in Smart Factories involves applying AI technologies 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 within smart factory environments.

Upon completing this training, participants will be able to:

• Grasp the fundamental principles of AI and machine learning.
• Recognize key AI use cases in manufacturing and automation.
• Explore how AI facilitates predictive maintenance, quality control, and process optimization.
• Assess 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 arrange customized training for this course, please contact us.

Implementing AI Use Cases employs a practical, project-focused methodology to apply machine learning, computer vision, and data analytics to address real-world industrial challenges, utilizing real or simulated datasets.

This guided, live training session (available online or in-person) targets intermediate-level cross-functional teams looking to collaboratively deploy AI solutions that align with their operational objectives, gaining valuable experience with industrial data pipelines.

Upon completion, participants will be equipped to:

• Identify and define practical AI use cases within operations, quality assurance, or maintenance.
• Collaborate across different roles to build machine learning solutions.
• Manage, clean, and analyze a variety of industrial datasets.
• Develop and present a functional prototype of an AI-driven solution based on a chosen use case.

Course Format

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

Customization Options

• To request a tailored training version of this course, please get in touch with us to arrange it.

This instructor-led, live training in Brazil (online or onsite) is designed for advanced cybersecurity professionals, AI engineers, and IoT developers looking to implement strong security measures and resilience strategies for Edge AI systems.

Upon completing this training, participants will be able to:

• Identify security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to protect data.
• Architect resilient Edge AI systems capable of withstanding cyber threats.
• Deploy AI models securely within edge environments.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed for optimized inference and training in both edge computing and data center environments.

This instructor-led, live training session (available online or on-site) is designed for intermediate-level developers who want to build and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completing this training, participants will be able to:

• Set up and configure development environments for BANGPy and Neuware.
• Develop and optimize Python- and C++-based models tailored for Cambricon MLUs.
• Deploy models to edge devices and data centers running the Neuware runtime.
• Integrate machine learning workflows with acceleration features specific to MLUs.

Course Format

• Interactive lectures and discussions.
• Hands-on practice using BANGPy and Neuware for development and deployment.
• Guided exercises focusing on optimization, integration, and testing.

Course Customization Options

• To request a customized training session tailored to your specific Cambricon device model or use case, please contact us to arrange.

Digital Twins are virtual representations of physical systems, augmented by real-time data and AI-powered analytics.

This instructor-led live training (available online or onsite) is designed for intermediate-level professionals looking to create, deploy, and optimize digital twin models leveraging real-time data and AI-driven insights.

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

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

Course Format

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

Customization Options

• For a customized training experience, please contact us to arrange.

Industrial computer vision combined with AI is reshaping the way manufacturers and quality assurance teams identify surface flaws, verify part compliance, and automate visual inspection workflows.

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

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

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

Course Format

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

Course Customization Options

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

Intelligent robotics involves embedding artificial intelligence into robotic systems to enhance perception, decision-making capabilities, and autonomous control.

This instructor-led training (available online or on-site) is designed for advanced robotics engineers, systems integrators, and automation leads who want to implement AI-driven perception, planning, and control within smart manufacturing environments.

Upon completing this training, participants will be able to:

• Comprehend and apply AI techniques for robotic perception and sensor fusion.
• Create motion planning algorithms for both collaborative and industrial robots.
• Implement learning-based control strategies to enable real-time decision-making.
• Integrate intelligent robotic systems into smart factory workflows.

Course Format

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

Customization Options

• For a customized training session, please contact us to make arrangements.