Fine-Tuning Lightweight Models for Edge AI Deployment

• An understanding of machine learning fundamentals
• Experience with Python and deep learning frameworks
• Familiarity with embedded systems or edge device constraints

Audience

• Embedded AI developers
• Edge computing specialists
• Machine learning engineers focusing on edge deployment

Model fine-tuning is the process of adapting pre-trained models to specific tasks or environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level embedded AI developers and edge computing specialists who wish to fine-tune and optimize lightweight AI models for deployment on resource-constrained devices.

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

• Select and adapt pre-trained models suitable for edge deployment.
• Apply quantization, pruning, and other compression techniques to reduce model size and latency.
• Fine-tune models using transfer learning for task-specific performance.
• Deploy optimized models on real edge hardware platforms.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Edge AI and Model Optimization

• Understanding edge computing and AI workloads
• Trade-offs: performance vs. resource constraints
• Overview of model optimization strategies

Model Selection and Pre-training

• Choosing lightweight models (e.g., MobileNet, TinyML, SqueezeNet)
• Understanding model architectures suitable for edge devices
• Using pre-trained models as a base

Fine-Tuning and Transfer Learning

• Principles of transfer learning
• Adapting models to custom datasets
• Practical fine-tuning workflows

Model Quantization

• Post-training quantization techniques
• Quantization-aware training
• Evaluation and trade-offs

Model Pruning and Compression

• Pruning strategies (structured vs. unstructured)
• Compression and weight sharing
• Benchmarking compressed models

Deployment Frameworks and Tools

• TensorFlow Lite, PyTorch Mobile, ONNX
• Edge hardware compatibility and runtime environments
• Toolchains for cross-platform deployment

Hands-On Deployment

• Deploying to Raspberry Pi, Jetson Nano, and mobile devices
• Profiling and benchmarking
• Troubleshooting deployment issues

Summary and Next Steps