Model Deployment

Model Deployment

Overview

Model deployment is the process of taking a trained machine learning model and making it available for production use through APIs, web services, or batch processing systems.

When to Use

• When productionizing trained models for real-world inference and predictions
• When building REST APIs or web services for model serving
• When scaling predictions to serve multiple users or applications
• When deploying models to cloud platforms, edge devices, or containers
• When implementing CI/CD pipelines for ML model updates
• When creating batch processing systems for large-scale predictions

Deployment Approaches

• REST APIs: Flask, FastAPI for synchronous inference
• Batch Processing: Scheduled jobs for large-scale predictions
• Real-time Streaming: Kafka, Spark Streaming for continuous data
• Serverless: AWS Lambda, Google Cloud Functions
• Edge Deployment: TensorFlow Lite, ONNX for edge devices
• Model Serving: TensorFlow Serving, Seldon Core, BentoML

Key Considerations