TinyML for IoT Applications Training Course

This instructor-led live training in Kenya (online or onsite) is aimed at 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.

This instructor-led course at Kenya empowers advanced professionals to design, optimize, and implement complete TinyML pipelines. Through practical labs, learners will acquire the ability to gather data, train energy-efficient models, and validate real-world applications.

This instructor-led, live training in Kenya (online or onsite) is aimed at intermediate-level IT professionals and business managers who wish to understand the potential of IoT and edge computing for enabling efficiency, real-time processing, and innovation in various industries.

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

• Understand the principles of IoT and edge computing and their role in digital transformation.
• Identify use cases for IoT and edge computing in manufacturing, logistics, and energy sectors.
• Differentiate between edge and cloud computing architectures and deployment scenarios.
• Implement edge computing solutions for predictive maintenance and real-time decision-making.

This instructor-led, live training in Kenya (online or onsite) is designed for intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI to enhance IoT applications with intelligent data processing and analytics capabilities.

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

• Understand the fundamentals of Edge AI and its application in IoT.
• Set up and configure Edge AI environments for IoT devices.
• Develop and deploy AI models on edge devices for IoT applications.
• Implement real-time data processing and decision-making in IoT systems.
• Integrate Edge AI with various IoT protocols and platforms.
• Address ethical considerations and best practices in Edge AI for IoT.

This instructor-led, live training in Kenya (online or onsite) is designed for intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

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

• Grasp the principles and advantages of Federated Learning in IoT and edge computing.
• Deploy Federated Learning models on IoT devices for decentralized AI processing.
• Minimize latency and enhance real-time decision-making in edge computing environments.
• Address challenges associated with data privacy and network constraints in IoT systems.

This instructor-led, live training conducted in Kenya (either online or onsite) targets intermediate-level embedded systems engineers and AI developers who aim to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

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

• Understand the fundamentals of TinyML and its benefits for edge AI applications.
• Set up a development environment for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
• Optimize AI models for power efficiency and memory constraints.

This instructor-led, live training in Kenya empowers advanced practitioners to fine-tune TinyML models for resource-limited embedded devices. Participants will apply quantization and pruning, develop low-latency inference pipelines, and measure performance against memory and energy boundaries.

This instructor-led course in Kenya empowers advanced professionals to safeguard TinyML pipelines on edge devices. Participants will acquire the skills to deploy privacy-preserving techniques, reinforce models against adversarial threats, and apply optimal practices for secure data processing in resource-limited settings.

This guided, live training in Kenya (online or in-person) is designed for novice engineers and data scientists who want to grasp the core concepts of TinyML, discover its practical uses, and deploy artificial intelligence models onto microcontrollers.

Upon completing this training, participants will be capable of:

• Grasping the fundamental principles of TinyML and its importance.
• Deploying efficient AI models on microcontrollers and edge hardware.
• Optimizing and refining machine learning models to minimize power usage.
• Utilizing TinyML for practical scenarios such as gesture recognition, anomaly detection, and audio processing.

This instructor-led, live training in Kenya empowers advanced professionals to integrate TinyML into autonomous robotics. Participants will learn to design optimized models, implement on-device perception pipelines, and deploy lightweight AI on embedded hardware to achieve real-time autonomy.

This instructor-led, live training Kenya (delivered online or on-site) targets intermediate-level embedded engineers, IoT developers, and AI researchers interested in implementing TinyML techniques for AI-powered applications on energy-efficient hardware.

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

• Comprehend the foundational aspects of TinyML and edge AI.

• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference for minimal power usage.
• Integrate TinyML with real-world IoT applications.

This instructor-led live training in Kenya focuses on deploying TinyML solutions for healthcare monitoring and diagnostics. Learners will acquire the skills to design models for real-time health data processing, optimize for low-power wearables, and ensure clinical reliability. The course includes practical lab exercises.

This 21-hour instructor-led course in Kenya empowers intermediate professionals to implement TinyML in smart agriculture. Discover how to build lightweight models, integrate edge AI with IoT, and optimize for precision irrigation and pest detection through hands-on lab work.