Advanced Techniques

Go beyond the basics. These techniques combine multiple SeeMe.ai features to solve complex real-world problems—faster labeling, smaller models, and production-grade pipelines.

What You’ll Learn

graph LR
    A[Automated Labeling] --> B[Model Distillation]
    B --> C[End-to-End Pipelines]
    C --> D[Production Optimization]
    style A fill:#f0f9ff,stroke:#0ea5e9
    style B fill:#f0fdf4,stroke:#22c55e
    style C fill:#fefce8,stroke:#eab308
    style D fill:#fdf2f8,stroke:#ec4899

Technique	What It Does	When To Use It
Automated Labeling	Use AI models to pre-label your data	You have lots of unlabeled data
Model Distillation	Train a small model from a large model’s outputs	You need speed/cost reduction in production
End-to-End Pipelines	Chain labeling → training → deployment	You want a repeatable, automated process
Production Optimization	Reduce latency, cost, and model size	You’re deploying at scale

Sections

Automated Labeling Model Distillation End-to-End Pipelines Production Optimization

The Big Picture

These techniques work together. A typical advanced workflow looks like this:

graph TD
    subgraph "1. Bootstrap Labels"
        A[Unlabeled Data] --> B[Large Model / LLM]
        B --> C[Auto-Generated Labels]
        C --> D[Human Review]
        D --> E[Verified Dataset]
    end

    subgraph "2. Distill"
        E --> F[Train Small Model]
        F --> G[Evaluate Against Large Model]
        G -->|Good enough| H[Deploy Small Model]
        G -->|Not yet| I[Add More Data]
        I --> D
    end

    subgraph "3. Optimize"
        H --> J[ONNX Export]
        J --> K[Quantization]
        K --> L[Production API]
        K --> M[Edge / Mobile]
    end

Start with Automated Labeling if you have unlabeled data, or jump to Model Distillation if you already have a large model producing good results.