Tiny Machine Learning

The TinyML category focuses on how Python is used to build, train, optimize, and prepare machine learning models for deployment on resource-constrained devices. This category highlights the Python tools and frameworks that make TinyML development practical and accessible. You’ll learn how Python fits into the TinyML workflow—from data preparation and model training to optimization and export for embedded inference.

What You’ll Learn

In this category, you’ll learn how Python libraries and frameworks are used in real TinyML projects.

By the end of TinyML, you’ll be able to:

  • Understand the Python-centric TinyML workflow
  • Use Python libraries to prepare and train lightweight models
  • Optimize models using Python tools
  • Export models for deployment on microcontrollers
  • Choose the right Python framework for TinyML tasks

Learning Path

Python in the TinyML Workflow

Learn how Python is used across the full TinyML lifecycle. Lessons include:

  • Overview of a Python-Based TinyML Workflow
  • Data Collection and Labeling with Python
  • Training Models in Python for Embedded Use
  • From Python Model to Embedded Inference
  • Common Pitfalls in Python-to-TinyML Pipelines

Data Processing and Feature Engineering

Learn how Python tools are used to prepare efficient inputs for TinyML models. Lessons include:

  • Feature Extraction with NumPy
  • Working with Time-Series and Sensor Data
  • Windowing and Signal Processing in Python
  • Reducing Feature Dimensionality
  • Preparing Data for Quantized Models

Machine Learning Frameworks for TinyML

Learn which Python frameworks are commonly used and why. Lessons include:

  • scikit-learn for Lightweight Models
  • TensorFlow and Keras for Small Neural Networks
  • When to Use Classical ML vs Neural Networks
  • Training Models with Embedded Constraints in Mind
  • Evaluating Model Size and Complexity

Model Optimization with Python

Learn how Python libraries are used to make models smaller and faster. Lessons include:

  • Quantization Concepts in Python
  • Model Pruning Basics
  • Reducing Precision and Memory Usage
  • Measuring Model Size and Latency
  • Tradeoffs Between Accuracy and Efficiency

Exporting and Deployment Preparation

Learn how Python models are prepared for deployment on embedded devices. Lessons include:

  • Exporting Models from Python
  • Introduction to TensorFlow Lite and TFLite Micro
  • Converting Models Using Python Tools
  • Verifying Model Outputs After Conversion
  • Preparing Artifacts for Embedded Teams

Who This Category Is For

This category is ideal if you:

  • Are comfortable with Python and basic machine learning
  • Want to use Python tools for TinyML development
  • Prefer working in Python before touching embedded code
  • Need a clear understanding of TinyML frameworks and workflows

Begin with the overview of Python-based TinyML workflows and follow the lessons in order. Each section focuses on practical Python tools that prepare your models for real-world TinyML deployment.