Building TinyML Pipelines Training in UK

The Building TinyML Pipelines Training is a hands-on, practical program focused on designing, training, deploying, and operating machine learning models on ultra-low-power, resource-constrained devices such as microcontrollers and edge sensors.

Participants will learn how to build end-to-end TinyML pipelines—from data collection and model training to optimization, deployment, and lifecycle management—enabling real-time intelligence at the edge for IoT, industrial, wearable, and embedded systems.

Welcome to the official Building TinyML Pipelines Training certification program. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge in in UK. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

• Basic programming knowledge (C/C++ or Python helpful)

• Familiarity with embedded systems or IoT concepts (recommended)

• Introductory understanding of ML concepts is helpful

• No advanced deep learning background required

• TinyML system architecture

• Sensor data collection and preprocessing

• Model training for embedded AI

• Model compression and optimization

• Microcontroller deployment

• Edge inference and monitoring

• Embedded AI lifecycle management

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

• Design complete TinyML pipelines for edge devices

• Collect and prepare sensor data for TinyML

• Train and optimize models for microcontrollers

• Deploy and run ML inference on constrained hardware

• Manage updates, monitoring, and lifecycle of TinyML systems

• Apply TinyML to real-world embedded and IoT use cases