Building End-to-End TinyML Pipelines Training Course

Foundations of TinyML Pipelines for Government

• Overview of TinyML Workflow Stages for Government
• Characteristics of Edge Hardware for Government Applications
• Pipeline Design Considerations for Government Use Cases

Data Collection and Preprocessing for Government

• Collecting Structured and Sensor Data for Government Projects
• Data Labeling and Augmentation Strategies for Government Datasets
• Preparing Datasets for Constrained Environments in Government Operations

Model Development for TinyML for Government

• Selecting Model Architectures for Microcontrollers in Government Applications
• Training Workflows Using Standard ML Frameworks for Government Use
• Evaluating Model Performance Indicators for Government Projects

Model Optimization and Compression for Government

• Quantization Techniques for Government Models
• Pruning and Weight Sharing in Government Applications
• Balancing Accuracy and Resource Limits for Government Operations

Model Conversion and Packaging for Government

• Exporting Models to TensorFlow Lite for Government Use
• Integrating Models into Embedded Toolchains for Government Projects
• Managing Model Size and Memory Constraints in Government Applications

Deployment on Microcontrollers for Government

• Flashing Models onto Hardware Targets for Government Use
• Configuring Run-Time Environments for Government Operations
• Real-Time Inference Testing in Government Applications

Monitoring, Testing, and Validation for Government

• Testing Strategies for Deployed TinyML Systems in Government Projects
• Debugging Model Behavior on Hardware for Government Use
• Performance Validation in Field Conditions for Government Operations

Integrating the Full End-to-End Pipeline for Government

• Building Automated Workflows for Government Projects
• Versioning Data, Models, and Firmware for Government Use
• Managing Updates and Iterations in Government Applications

Summary and Next Steps for Government