Edge AI Training Courses

Introduction to Edge AI in Healthcare• Definition and scope of Edge AI for healthcare• Difference between cloud-based and edge-based healthcare AI• Benefits of low latency, privacy, and on-device intelligence• Overview of Edge AI healthcare use casesHealthcare...

• Healthcare professionals and clinicians
• Digital health and health informatics teams
• AI and data science professionals in healthcare
• Medical device and health technology engineers
• Healthcare innovation and transformation leaders

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Edge AI for Healthcare Training, validating their expertise in deploying and evaluating Edge AI solutions for real-time, secure, and compliant healthcare applications.

Prerequisites: • Basic understanding of healthcare workflows or clinical environments• Familiarity with medical data or digital health systems is beneficial• General awareness of artificial intelligence concepts• Interest in real-time and privacy-focused healthcare AI

Introduction to Edge AI for Robotics• Definition and scope of Edge AI in robotic systems• Difference between cloud-based robotics and edge-enabled robotics• Benefits of low-latency, on-device intelligence• Overview of Edge AI robotics use casesRobotic Hardware...

• Robotics and automation engineers
• AI and machine learning professionals working on robotic systems
• Embedded systems and edge computing engineers
• Researchers in robotics and autonomous systems
• Technology professionals building intelligent robotic solutions

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Edge AI for Robotics Training, validating their expertise in deploying and managing Edge AI solutions for real-time, autonomous, and intelligent robotic systems.

Prerequisites: • Basic understanding of artificial intelligence or machine learning concepts• Familiarity with robotics, automation, or autonomous systems• General awareness of sensors, embedded systems, or control architectures• Interest in real-time and autonomous robotic intelligence

Introduction to Edge AI for Smart Cities• Definition and scope of Edge AI in urban environments• Difference between cloud-based and edge-based smart city AI• Benefits of low latency, scalability, and resilience• Overview of smart city Edge AI use casesSmart Ci...

• Smart city planners and urban development professionals
• Government and public sector technology teams
• IoT and edge computing engineers
• AI and data science professionals working on city-scale systems
• Infrastructure, mobility, and sustainability leaders

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Edge AI for Smart Cities Training, validating their expertise in applying Edge AI concepts to intelligent urban infrastructure, real-time analytics, and smart city solutions.

Prerequisites: • Basic understanding of smart city concepts or urban infrastructure• Familiarity with IoT, sensors, or connected systems• General awareness of artificial intelligence or data analytics• Interest in real-time and large-scale urban AI solutions

Introduction to Edge AI• Definition and scope of Edge AI• Difference between cloud AI and Edge AI• Benefits of edge intelligence such as low latency and privacy• Overview of Edge AI use casesEdge AI Architecture and Hardware• Edge devices, sensors, and gateway...

• AI and machine learning professionals
• IoT and embedded systems engineers
• Edge computing and infrastructure specialists
• Robotics and automation professionals
• Technology leaders exploring decentralized AI

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Edge AI Fundamentals Training, validating their expertise in understanding Edge AI concepts, architectures, deployment considerations, and real-world applications.

Prerequisites: • Basic understanding of artificial intelligence or machine learning concepts• Familiarity with IoT devices, embedded systems, or edge computing concepts• General awareness of data processing and analytics• Interest in real-time and distributed AI systems

Introduction to Edge AI in Financial Services• Definition and scope of Edge AI for finance• Difference between cloud-based and edge-based financial AI• Benefits of low latency, privacy, and resilience• Overview of financial Edge AI use casesFinancial Edge Arch...

• Banking and financial services professionals
• Risk, fraud, and compliance teams
• Fintech and financial technology engineers
• AI and data science professionals in finance
• Digital transformation leaders in financial institutions

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Financial Edge AI Training, validating their expertise in applying Edge AI concepts to real-time, secure, and compliant financial services applications.

Prerequisites: • Basic understanding of finance, banking, or financial services• Familiarity with financial transactions, data, or systems• General awareness of artificial intelligence or data analytics• Interest in real-time and privacy-focused financial AI solutions

Introduction to Edge AI in Industrial Automation• Definition and scope of Edge AI in industrial environments• Difference between cloud-based analytics and Edge AI systems• Benefits of low latency, reliability, and on-device intelligence• Overview of industrial...

• Industrial automation and control engineers
• Manufacturing and plant operations professionals
• AI and data science professionals in industrial domains
• IoT, embedded systems, and edge computing engineers
• Industry 4.0 and digital transformation leaders

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Industrial Edge AI Training, validating their expertise in applying Edge AI concepts to industrial automation, real-time intelligence, and smart manufacturing systems.

Prerequisites: • Basic understanding of industrial automation or manufacturing processes• Familiarity with sensors, PLCs, SCADA, or industrial control systems• General awareness of artificial intelligence or data analytics• Interest in real-time and intelligent industrial systems