Getting Started
This guide walks you through selecting hardware, configuring your development environment, and running your first embedded ML application. Most students complete setup in under an hour.
Step 1: Select Your Platform
Your choice depends on budget, learning objectives, and the types of applications you want to build.
For beginners or budget-conscious learners:
| Platform | Cost | Why Choose It |
|---|---|---|
| Grove Vision AI V2 | ~$25 | No-code interface, fastest path to running models |
| XIAOML Kit | ~$40 | Best value, supports vision, audio, and motion |
For advanced applications:
| Platform | Cost | Why Choose It |
|---|---|---|
| Raspberry Pi | ~$60-80 | Full Linux environment, LLMs and VLMs |
| Nicla Vision | ~$95 | Professional-grade, ultra-low power design |
For detailed specifications and technical comparisons, see Platforms.
Step 2: Set Up Your Environment
Development environment configuration is platform-dependent but follows a common pattern: install software tools, configure communication with hardware, and verify the setup works.
Time estimate: 30-60 minutes depending on platform and internet speed.
Follow the IDE Setup Guide for complete procedures covering:
- System requirements for your development computer
- Arduino IDE installation for microcontroller platforms
- Python environment configuration for Raspberry Pi
- SenseCraft AI web interface for Grove Vision AI V2
- Serial communication and hardware verification
Step 3: Choose Your First Lab
Each platform supports different exercise categories. Select labs that match both your hardware and learning goals.
| Lab Category | Grove Vision | XIAOML Kit | Nicla | Raspberry Pi |
|---|---|---|---|---|
| Image Classification | ✓ | ✓ | ✓ | ✓ |
| Object Detection | ✓ | ✓ | ✓ | ✓ |
| Keyword Spotting | ✓ | ✓ | ||
| Motion Classification | ✓ | ✓ | ||
| Large Language Models | ✓ | |||
| Vision Language Models | ✓ |
Step 4: Start Your First Lab
Grove Vision AI V2: Begin with Setup and No-Code Apps. You’ll deploy a pre-trained model in minutes using the visual interface.
XIAOML Kit: Start with Setup, then proceed to Image Classification to train and deploy your first custom model.
Nicla Vision: Complete Setup to configure your board, then try Image Classification.
Raspberry Pi: Follow Setup, then choose your path: - Image Classification for computer vision fundamentals - LLM Deployment to run language models on edge hardware
Prerequisites
These labs assume:
- Programming: Proficiency in Python. Familiarity with C/C++ is helpful for microcontroller platforms but not required.
- Mathematics: Working knowledge of linear algebra and basic probability at the undergraduate level.
- Hardware: No prior embedded systems experience. Each lab includes complete setup and troubleshooting procedures.
Connection to ML Systems Textbook
These laboratories complement specific chapters in the ML Systems textbook:
- Image Classification labs reinforce concepts from the Computer Vision and Model Optimization chapters
- Keyword Spotting labs connect to Audio Processing and Real-time Inference
- Motion Classification labs demonstrate Sensor Fusion and Time-series Analysis
- LLM/VLM labs extend Large Model Deployment to resource-constrained environments
Each lab identifies relevant textbook sections for deeper theoretical understanding.