Model deployments

The critical MLOps phase: placing a trained machine learning model into a production environment for real-time or batch inference.

Model deployment is the final, essential step that moves a validated ML asset from development to real-world application (e.g., fraud detection, product recommendations). This process makes the model’s predictive power available to end-users or other systems via an API endpoint. Key steps include model optimization and packaging the model and its dependencies using containerization (Docker) for consistent execution. Orchestration tools like Kubernetes manage scaling, ensuring the system handles high-volume requests with low latency. Advanced strategies like A/B testing or Canary deployments are used to safely roll out new model versions, minimizing risk and ensuring performance (e.g., comparing Model A vs. Model B on live traffic). Successful deployment ensures the model drives measurable business value, not just research milestones.

https://www.ibm.com/topics/model-deployment