Loading content
Concept and practice for reliably delivering, operating and versioning trained machine learning models in production environments.
Model deployment describes the process of moving trained ML models into production environments, serving predictions and operating them reliably. It covers packaging, serving, scaling, monitoring and versioning to ensure repeatable inference. It also addresses security, integration and operational governance requirements.
95th percentile of response time for inference requests; important for user experience.
Quality measure of predictions on production data or proxy sets.
Share of failed or rejected requests of total traffic.
Registering, versioning and serving a model as a REST endpoint with MLflow.
Deploying and scaling a TensorFlow SavedModel in TensorFlow Serving.
Orchestrating containerized models on Kubernetes with Seldon Core and using versioned routing.
Package the model and pin dependencies; register artifact.
Create deployment artifacts (container, manifests) and set up CI/CD pipeline.
Provision serving endpoint, run tests, configure monitoring and plan rollout.