TinyML for IoT Applications Training Course

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

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

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

This 21-hour training in Botswana empowers IT architects to design next-generation distributed systems. By exploring 6G, edge computing, and AI integration, you will learn to build low-latency, scalable infrastructures. Develop practical skills to create secure, resilient, and intelligent edge architectures that meet future business demands.

This instructor-facilitated course in Botswana empowers advanced professionals to architect, optimize, and deploy holistic TinyML pipelines. Learners will develop the ability to gather data, train low-power models, and evaluate practical applications through hands-on laboratory sessions.

This instructor-led, live training in Botswana (online or on-site) is designed for 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 Botswana (online or onsite) is aimed at intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI for enhancing 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 Botswana (online or onsite) is aimed at intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

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

• Grasp the principles and benefits of Federated Learning in IoT and edge computing.
• Implement Federated Learning models on IoT devices for decentralized AI processing.
• Reduce latency and improve real-time decision-making in edge computing environments.
• Address challenges related to data privacy and network constraints in IoT systems.

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

By the end 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 live, instructor-led course in Botswana empowers advanced practitioners to optimize TinyML models for embedded devices with limited resources. Learners will apply quantization and pruning techniques, build low-latency inference pipelines, and evaluate performance against memory and energy constraints.

This instructor-led training in Botswana empowers advanced professionals to secure TinyML pipelines on edge devices. You will master the implementation of privacy-preserving techniques, learn to harden models against adversarial threats, and apply best practices for secure data handling in constrained environments.

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

Upon completing this training, participants will be capable of:

• Grasping the core principles of TinyML and its importance.
• Deploying streamlined AI models on microcontrollers and edge devices.
• Optimising and fine-tuning machine learning models for low-power usage.
• Applying TinyML in real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

This instructor-led live training in Botswana guides advanced professionals in integrating 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 in Botswana (online or at your premises) targets intermediate-level embedded engineers, IoT developers, and AI researchers looking to apply TinyML techniques for AI-driven applications on energy-efficient hardware.

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

• Grasping the core principles of TinyML and edge AI.
• Deploying lightweight AI models onto microcontrollers.
• Optimising AI inference to minimise power usage.
• Integrating TinyML into practical IoT applications.

This instructor-led live training in Botswana focuses on implementing TinyML solutions for healthcare monitoring and diagnostics. Participants will learn to design models for real-time health data, optimize them for low-power wearables, and ensure clinical reliability. The course includes hands-on lab exercises.

This 21-hour instructor-led course in Botswana empowers intermediate professionals to implement 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 in a hands-on lab setting.