⚙️Engineering
🚀
Model Deployment
Learn to deploy ML models as scalable APIs and services
Prerequisites
🌱
Beginner
BeginnerDeployment fundamentals
What to Learn
- •REST APIs for ML (FastAPI, Flask)
- •Model serialization formats
- •Docker basics for ML
- •Cloud deployment (AWS, GCP, Azure)
- •Serverless ML deployment
Resources
- 📚FastAPI ML tutorial
- 📚Docker for Data Science
- 📚AWS SageMaker getting started
🌿
Intermediate
IntermediateScalable deployment patterns
What to Learn
- •Kubernetes for ML workloads
- •Load balancing and auto-scaling
- •Model serving frameworks (TorchServe, TF Serving)
- •Batching strategies for inference
- •GPU resource management
Resources
- 📚KServe documentation
- 📚TorchServe tutorials
- 📚NVIDIA Triton Inference Server
🌳
Advanced
AdvancedAdvanced deployment architectures
What to Learn
- •Multi-model serving architectures
- •Edge deployment and optimization
- •Real-time vs batch inference
- •Canary deployments and rollbacks
- •Global model serving infrastructure
Resources
- 📚Ray Serve documentation
- 📚BentoML for ML serving
- 📚Infrastructure papers from big tech