TinyML with Raspberry Pi and Arduino Training Course

This instructor-led training in Brazil empowers advanced professionals with the capabilities to design, refine, and deploy comprehensive TinyML pipelines. Through practical labs, participants will master data collection, low-power model training, and real-world application testing.

This instructor-led, live training in Brazil (online or onsite) targets 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 instructor-led, live training in Brazil prepares advanced practitioners to optimize TinyML models for resource-limited embedded devices. Learners will master quantization and pruning, implement low-latency inference pipelines, and benchmark performance against strict memory and energy boundaries.

This instructor-led training in Brazil empowers advanced professionals to safeguard TinyML pipelines on edge devices. You will learn to implement privacy-preserving strategies, reinforce models against adversarial threats, and apply best practices for secure data management in constrained environments.

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

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

• Understand the fundamentals of TinyML and its significance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models for low-power consumption.
• Apply TinyML for real-world applications such as gesture recognition, anomaly detection, and audio processing.

This instructor-led, live training in Brazil 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 Brazil (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who wish to implement TinyML techniques for AI-powered applications on energy-efficient hardware.

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

• Understand the fundamentals of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference for low-power consumption.
• Integrate TinyML with real-world IoT applications.

This instructor-led live training in Brazil focuses on implementing 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. The course includes hands-on lab exercises.

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

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

• Grasp the fundamentals of TinyML and its role in IoT.
• Configure a TinyML development environment for IoT projects.
• Create and deploy ML models on low-power microcontrollers.
• Apply TinyML for predictive maintenance and anomaly detection.
• Optimize TinyML models to ensure efficient power and memory usage.

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