Standard 3 stops to get here · leads to 9
Model Serving
Deploying trained models as services that can handle prediction requests in production environments.
Your route here
3 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Training ✓ understood
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
- Inference ✓ understood
Running a trained model on new inputs to get predictions, with its weights frozen: the stage of a model's life that users actually interact with.
- Model Serving · you are here ✓ understood
Where it sits
Before this
Inference Model Serving
Leads to
A/B Testing MLOps Model Endpoint Model Monitoring Blue-Green Deployment Canary Deployment+ 3 more
Explore nearby
Shipping AI Model Endpoint A deployed service exposing a model's predictions via API requests. Shipping AI REST API A web service interface commonly used for serving model predictions over HTTP. Shipping AI gRPC A high-performance RPC framework often used for low-latency model serving. Shipping AI Request Batching Combining multiple inference requests into batches to improve throughput. Shipping AI Model Caching Storing frequently requested predictions to reduce latency and computation. Shipping AI MLOps Practices for deploying, monitoring, and maintaining machine learning models in production, combining ML and DevOps principles.