TinyML: Running AI on Ultra-Low-Power Edge Devices Training Course

This instructor-led, live training in India (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to explore how 5G networks accelerate Edge AI applications.

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

• Understand the fundamentals of 5G technology and its impact 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.

The '6G and the Intelligent Edge' course offers a forward-looking perspective on how 6G wireless technologies merge with edge computing, IoT ecosystems, and AI-driven data processing to create intelligent, adaptive infrastructures with ultra-low latency.

This instructor-led live training, available both online and onsite, is tailored for intermediate-level IT architects aiming to grasp and design next-generation distributed architectures that harness the synergy between 6G connectivity and intelligent edge systems.

After completing this course, participants will be equipped to:

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

Course Format

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

Customization Options

• To request a customized training session for this course, please get in touch with us to arrange it.

This instructor-led live training in India (online or on-site) 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 sectors.

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

• Investigate advanced methods for developing and optimizing Edge AI models.
• Apply cutting-edge strategies for deploying AI models on edge devices.
• Leverage specialized tools and frameworks for complex Edge AI applications.
• Enhance the performance and efficiency of Edge AI solutions.
• Explore innovative use cases and emerging trends in the Edge AI landscape.
• Tackle advanced ethical and security challenges associated with Edge AI deployments.

This instructor-led live training in India (online or onsite) targets intermediate-level developers, data scientists, and tech enthusiasts seeking practical skills in deploying AI models on edge devices for diverse applications.

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

• Comprehend the core principles of Edge AI and its advantages.
• Establish and configure the edge computing environment.
• Develop, train, and optimize AI models specifically for edge deployment.
• Deploy practical AI solutions on edge devices.
• Assess and enhance the performance of models deployed on the edge.
• Tackle ethical and security issues inherent in Edge AI applications.

TinyML involves the deployment of optimized machine learning models onto edge devices with limited resources.

This instructor-led live training, available both online and onsite, targets advanced technical professionals aiming to design, optimize, and implement comprehensive TinyML pipelines.

Upon completing this training, participants will acquire the skills to:

• Gather, prepare, and manage datasets for TinyML use cases.
• Train and fine-tune models for low-power microcontrollers.
• Transform models into lightweight formats compatible with edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Delivery Format

• Lectures guided by instructors alongside technical discussions.
• Practical lab sessions and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Customization Options

• For tailored training involving specific toolchains, hardware boards, or internal workflows, please reach out to us to make arrangements.

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

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

• Grasp the 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 secure AI models within edge environments.

This instructor-led, live training in India (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers looking to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be able to:

• Comprehend the core principles of TinyML and its advantages for edge AI applications.
• Configure a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Utilize TensorFlow Lite and Edge Impulse to build real-world TinyML applications.
• Enhance AI models for power efficiency and manage memory constraints effectively.

TinyML involves the deployment of machine learning models onto hardware with stringent resource limitations.

This instructor-led, live training (available online or onsite) is designed for advanced practitioners seeking to optimize TinyML models for low-latency, memory-efficient deployment on embedded devices.

Upon completing this training, participants will be able to:

• Utilize quantization, pruning, and compression methods to minimize model size while preserving accuracy.
• Benchmark TinyML models to assess latency, memory usage, and energy efficiency.
• Deploy optimized inference pipelines on microcontrollers and edge devices.
• Assess the trade-offs between performance, accuracy, and hardware constraints.

Course Format

• Instructor-led presentations complemented by technical demonstrations.
• Practical optimization exercises and comparative performance testing.
• Hands-on implementation of TinyML pipelines within a controlled lab environment.

Customization Options

• For training tailored to specific hardware platforms or internal workflows, please contact us to customize the program.

TinyML involves deploying machine learning models on low-power, resource-constrained devices at the network edge.

This instructor-led live training, available both online and onsite, is designed for advanced professionals aiming to secure TinyML pipelines and integrate privacy-preserving techniques into edge AI applications.

Upon completing this course, participants will be capable of:

• Identifying security risks specific to on-device TinyML inference.
• Implementing privacy-preserving mechanisms for edge AI deployments.
• Strengthening TinyML models and embedded systems against adversarial threats.
• Applying best practices for secure data handling in constrained environments.

Course Format

• Interactive lectures accompanied by expert-led discussions.
• Practical exercises focusing on real-world threat scenarios.
• Hands-on implementation using embedded security tools and TinyML frameworks.

Course Customization Options

• Organizations can request a customized version of this training to meet their specific security and compliance requirements.

This instructor-led, live training in India (online or onsite) is designed for beginner-level engineers and data scientists who want to grasp the fundamentals of TinyML, explore its practical uses, and deploy AI models on microcontrollers.

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

• Grasp the core concepts of TinyML and its importance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models to minimize power usage.
• Implement TinyML in real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

TinyML serves as a framework for deploying machine learning models onto low-power microcontrollers and embedded platforms within robotics and autonomous systems.

This instructor-led live training, available online or onsite, targets advanced professionals seeking to incorporate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

After completing this course, participants will be equipped to:

• Design optimized TinyML models tailored for robotics applications.
• Implement on-device perception pipelines for real-time autonomy.
• Integrate TinyML into existing robotic control frameworks.
• Deploy and test lightweight AI models on embedded hardware platforms.

Format of the Course

• Technical lectures combined with interactive discussions.
• Hands-on labs focusing on embedded robotics tasks.
• Practical exercises simulating real-world autonomous workflows.

Course Customization Options

• For organization-specific robotics environments, customization can be arranged upon request.

TinyML refers to the integration of machine learning capabilities into low-power, resource-constrained wearable and medical devices.

This instructor-led training session, available both online and onsite, is designed for intermediate-level professionals aiming to implement TinyML solutions for healthcare monitoring and diagnostic applications.

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

• Design and deploy TinyML models for real-time health data processing.
• Collect, preprocess, and interpret biosensor data to derive AI-driven insights.
• Optimize models specifically for low-power and memory-constrained wearable devices.
• Assess the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

• Lectures supplemented with live demonstrations and interactive discussions.
• Practical exercises involving wearable device data and TinyML frameworks.
• Guided implementation exercises within a lab environment.

Customization Options

• For training tailored to specific healthcare devices or regulatory workflows, please contact us to customize the program.

This instructor-led, live training in India (online or onsite) targets intermediate-level IoT developers, embedded engineers, and AI professionals who want to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

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

• Grasp the fundamentals of TinyML and its applications in IoT.
• Set up a TinyML development environment for IoT projects.
• Create and deploy ML models on low-power microcontrollers.
• Implement predictive maintenance and anomaly detection using TinyML.
• Optimize TinyML models for efficient power and memory usage.

TinyML refers to a machine learning methodology fine-tuned for compact, resource-limited devices.

This trainer-led live session, available online or at your venue, is designed for learners at beginner to intermediate levels who aim to develop functional TinyML applications using Raspberry Pi, Arduino, and comparable microcontrollers.

After finishing this training, participants will acquire the ability to:

• Gather and prepare data specifically for TinyML initiatives.
• Train and optimize compact machine learning models for microcontroller settings.
• Deploy TinyML models onto Raspberry Pi, Arduino, and associated boards.
• Create complete end-to-end embedded AI prototypes.

Course Format

• Trainer-led lectures and guided discussions.
• Practical activities and hands-on experimentation.
• Live-lab project work involving actual hardware.

Customization Options for the Course

• For bespoke training that aligns with your specific hardware or application requirements, please reach out to us to make arrangements.

TinyML enables the deployment of machine learning models on low-power, resource-constrained devices directly in the field.

This instructor-led live training, available online or onsite, is designed for intermediate-level professionals looking to apply TinyML techniques to smart agriculture solutions that enhance automation and environmental intelligence.

Upon completing this program, participants will be able to:

• Build and deploy TinyML models for agricultural sensing applications.
• Integrate edge AI into IoT ecosystems for automated crop monitoring.
• Utilize specialized tools to train and optimize lightweight models.
• Develop workflows for precision irrigation, pest detection, and environmental analytics.

Format of the Course

• Guided presentations coupled with applied technical discussions.
• Hands-on practice using real-world datasets and devices.
• Practical experimentation in a supported lab environment.

Course Customization Options

• For tailored training aligned with specific agricultural systems, please contact us to customize the program.