Catalog
concept#Machine Learning#Platform#DevOps#Observability

Model Deployment

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.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

CI/CD systems (e.g. Jenkins, GitHub Actions)Monitoring stack (Prometheus, Grafana)Model registry / artifact store

Principles & goals

Reproducibility through versioning of model and data.Automated deployment with verifiable tests.Observability and alerting as an operational obligation.
Run
Domain, Team

Use cases & scenarios

Compromises

  • Model drift without monitoring leads to quality degradation.
  • Insecure endpoints jeopardize privacy and integrity.
  • Complex rollouts can lead to outages.
  • Automated end-to-end tests including smoke and regression tests.
  • Consistent model and data versioning in a registry.
  • Use shadowing or canary deployments before full rollout.

I/O & resources

  • Trained model artifact
  • Dependency package / runtime environment
  • Deployment manifest (container, deployment config)
  • Available API endpoints
  • Monitoring metrics and logs
  • Versioned model artifacts in registry

Description

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.

✔

  • Faster time-to-market for models.
  • More stable prediction services through standardized processes.
  • Improved traceability and governance of models.

✖

  • Dependency on infrastructure and operational processes.
  • Costly testing when data or schema change.
  • Not all models are suitable for real-time serving.

  • Latency p95

    95th percentile of response time for inference requests; important for user experience.

  • Hit rate / Accuracy

    Quality measure of predictions on production data or proxy sets.

  • Traffic error rate

    Share of failed or rejected requests of total traffic.

Using MLflow for model serving

Registering, versioning and serving a model as a REST endpoint with MLflow.

TensorFlow Serving for real-time inference

Deploying and scaling a TensorFlow SavedModel in TensorFlow Serving.

Kubernetes + Seldon Core for model-based APIs

Orchestrating containerized models on Kubernetes with Seldon Core and using versioned routing.

1

Package the model and pin dependencies; register artifact.

2

Create deployment artifacts (container, manifests) and set up CI/CD pipeline.

3

Provision serving endpoint, run tests, configure monitoring and plan rollout.

⚠️ Technical debt & bottlenecks

  • Hard-coded model loading duplicated across repositories.
  • Missing automation for rollbacks and migration paths.
  • Incomplete test coverage for inference paths.
Model size and inference latencyData pipeline latencyInfrastructure capacity
  • Using a heavy research model unchanged for real-time serving, causing high latency.
  • Operating production with experimental models without governance.
  • Configuring training and serving environments inconsistently and risking inference errors.
  • Underestimating non-functional requirements like latency and throughput.
  • Ignoring model drift and lack of alerting.
  • Unconsidered dependencies between feature pipeline and serving code.
Knowledge of containerization and orchestrationUnderstanding of ML model formats and serializationExperience with monitoring and observability
Scalability for inference loadAvailability and fault toleranceSecurity and compliance
  • • Limited compute resources in the target environment.
  • • Privacy and compliance requirements.
  • • Incompatible dependencies between training and serving.