ML Engineering
Model Deployment - Serving ML Models at Scale
Learn how to deploy machine learning models to production and serve them reliably at scale.
- Serving architectures - REST APIs, batch inference, edge deployment
- Scalability - handle millions of predictions per day
- Model optimization - quantization, pruning, and distillation
The goal is not to build a model, but to deploy value.
Model Deployment — Complete Guide
Deploying ML models to production requires APIs, containers, monitoring, and scalability.
Deployment Options
Deployment Architecture Overview
FastAPI Deployment
Docker
Container Orchestration with Kubernetes
Key Takeaways
What to Learn Next
-> MLOps Master the full ML lifecycle.
-> ML System Design Design production ML systems.
-> Model Evaluation Measure and monitor model performance.
-> A/B Testing Compare model versions scientifically.
-> Model Selection Choose the best model for deployment.
-> AutoML Automate model selection and tuning.