Integrating Machine Learning Models
Having established how to define and validate data structures with Pydantic, the next step is to integrate the machine learning models themselves into your FastAPI application. This chapter addresses the practicalities of bridging the gap between trained models and live API endpoints.
You will learn methods for:
- Serializing trained models for storage and deserializing them for use.
- Loading model artifacts efficiently within the FastAPI application lifecycle.
- Creating API endpoints designed specifically for receiving input data and returning model predictions.
- Managing the model files (artifacts) associated with your application.
- Using FastAPI's dependency injection mechanism to provide models to your prediction logic.
By the end of this chapter, you will be able to build functional API endpoints that serve predictions from your trained machine learning models.
Sections
3.1 Serializing and Deserializing ML Models
3.2 Loading Models into FastAPI Applications
3.3 Creating Prediction Endpoints
3.4 Handling Different Input Formats
3.5 Returning Predictions and Probabilities
3.6 Managing Model Artifacts
3.7 Dependency Injection for Model Loading
3.8 Practice: Building a Model Prediction Service
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