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

This instructor-led, live training in Malaysia (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists keen to explore how 5G networks accelerate 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 tailored for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimise AI workloads for efficient performance on edge devices.

This 21-hour training in Malaysia empowers IT architects to design next-generation distributed systems. Discover the integration of 6G, edge computing, and AI to build low-latency, scalable infrastructures. Acquire practical skills to create secure, resilient, and intelligent edge architectures that address future business needs.

This instructor-led, live training in Malaysia (online or on-site) is aimed at advanced-level AI practitioners, researchers, and developers who wish to master the latest advancements in Edge AI, optimize their AI 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 in Edge AI model development and optimization.
• Implement cutting-edge strategies for deploying AI models on edge devices.
• Utilize specialized tools and frameworks for advanced Edge AI applications.
• Optimize performance and efficiency of Edge AI solutions.
• Explore innovative use cases and emerging trends in Edge AI.
• Address advanced ethical and security considerations in Edge AI deployments.

This instructor-led live training in Malaysia (available online or on-site) is designed for intermediate-level developers, data scientists, and tech enthusiasts looking to acquire practical skills in deploying AI models on edge devices for a variety of applications.

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

• Understand the principles of Edge AI and its benefits.
• Set up and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

This instructor-led course in Malaysia empowers advanced professionals to architect, optimize, and launch complete TinyML pipelines. Learners will acquire the skills to gather data, train energy-efficient models, and test real-world applications through hands-on labs.

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

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

• Understand security risks and vulnerabilities in Edge AI deployments.
• Implement encryption and authentication techniques for data protection.
• Design resilient Edge AI architectures that can withstand cyber threats.
• Apply secure AI model deployment strategies in edge environments.

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

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

• Grasp the fundamentals of TinyML and its advantages for edge AI applications.
• Establish a development environment for TinyML projects.
• Train, optimise, and deploy AI models on low-power microcontrollers.
• Utilise TensorFlow Lite and Edge Impulse to build real-world TinyML applications.
• Optimize AI models to meet power efficiency and memory constraints.

Delivered in Malaysia, this live instructor-led training empowers advanced practitioners to refine TinyML models for resource-limited embedded devices. Learners will apply quantization and pruning techniques, construct low-latency inference pipelines, and evaluate performance relative to memory and energy constraints.

This instructor-led training in Malaysia empowers advanced professionals to secure TinyML pipelines on edge devices. You will gain the skills to implement privacy-preserving techniques, reinforce models against adversarial attacks, and apply best practices for secure data handling in resource-constrained settings.

This instructor-led live training in Malaysia (online or onsite) is designed for beginner-level engineers and data scientists who want to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.

Upon completing this training, participants will be able to:

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

This instructor-led, live training in Malaysia guides advanced professionals through the integration of TinyML into autonomous robotics. Participants will learn to design optimized models, establish on-device perception pipelines, and deploy lightweight AI on embedded hardware to achieve real-time autonomy.

This live, instructor-led training in Malaysia focuses on deploying TinyML solutions for healthcare monitoring and diagnostics. Learners will master the design of models for real-time health data, optimization for low-power wearables, and ensuring clinical reliability, supported by hands-on lab exercises.

This instructor-led, live training in Malaysia (online or onsite) is aimed at intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

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

• Understand the fundamentals of TinyML and its applications in IoT.
• Set up a TinyML development environment for IoT projects.
• Develop 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.

This 21-hour instructor-led course in Malaysia empowers intermediate professionals to deploy TinyML for smart agriculture. Participants will learn to build lightweight models, integrate edge AI with IoT, and optimize systems for precision irrigation and pest detection within a hands-on lab setting.