What is Model Packaging?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Include model artifacts including weights and configuration, inference code, dependency specifications with pinned versions, preprocessing and postprocessing logic, sample input/output for validation, health check endpoints, and deployment configuration. Use container images (Docker) as the standard packaging format since they capture the complete runtime environment. Include a model card documenting capabilities, limitations, and intended use. The goal is that anyone can deploy the package without additional context.

Use Docker containers for production deployment since they guarantee environment consistency across development, staging, and production. Use framework-specific formats like SavedModel or ONNX for model exchange between teams. The best practice is to package the framework-specific model inside a Docker container that includes the serving code. Tools like BentoML, MLflow Models, and Seldon Core automate this packaging. Container-based deployment adds 50-100MB overhead but eliminates dependency conflicts.

Store model packages in a container registry like Docker Hub, ECR, or GCR with semantic versioning tags. Use immutable tags so a specific version always produces the same behavior. Include model metadata as container labels including training date, dataset version, and performance metrics. Maintain a model registry that maps versions to their lineage and evaluation results. Never overwrite existing version tags since this breaks reproducibility and makes rollbacks unreliable.

Include model artifacts including weights and configuration, inference code, dependency specifications with pinned versions, preprocessing and postprocessing logic, sample input/output for validation, health check endpoints, and deployment configuration. Use container images (Docker) as the standard packaging format since they capture the complete runtime environment. Include a model card documenting capabilities, limitations, and intended use. The goal is that anyone can deploy the package without additional context.

Use Docker containers for production deployment since they guarantee environment consistency across development, staging, and production. Use framework-specific formats like SavedModel or ONNX for model exchange between teams. The best practice is to package the framework-specific model inside a Docker container that includes the serving code. Tools like BentoML, MLflow Models, and Seldon Core automate this packaging. Container-based deployment adds 50-100MB overhead but eliminates dependency conflicts.

Store model packages in a container registry like Docker Hub, ECR, or GCR with semantic versioning tags. Use immutable tags so a specific version always produces the same behavior. Include model metadata as container labels including training date, dataset version, and performance metrics. Maintain a model registry that maps versions to their lineage and evaluation results. Never overwrite existing version tags since this breaks reproducibility and makes rollbacks unreliable.

Include model artifacts including weights and configuration, inference code, dependency specifications with pinned versions, preprocessing and postprocessing logic, sample input/output for validation, health check endpoints, and deployment configuration. Use container images (Docker) as the standard packaging format since they capture the complete runtime environment. Include a model card documenting capabilities, limitations, and intended use. The goal is that anyone can deploy the package without additional context.

Use Docker containers for production deployment since they guarantee environment consistency across development, staging, and production. Use framework-specific formats like SavedModel or ONNX for model exchange between teams. The best practice is to package the framework-specific model inside a Docker container that includes the serving code. Tools like BentoML, MLflow Models, and Seldon Core automate this packaging. Container-based deployment adds 50-100MB overhead but eliminates dependency conflicts.

Store model packages in a container registry like Docker Hub, ECR, or GCR with semantic versioning tags. Use immutable tags so a specific version always produces the same behavior. Include model metadata as container labels including training date, dataset version, and performance metrics. Maintain a model registry that maps versions to their lineage and evaluation results. Never overwrite existing version tags since this breaks reproducibility and makes rollbacks unreliable.