Model Deployment

Model deployment is the process of integrating a trained machine learning or deep learning model into an actual application or service and making it available to users or systems. In the overall AI development workflow — training → evaluation → production — it represents the final, critical step. Model deployment involves objectives and technical challenges such as: • Running real-time predictions (e.g., a chatbot responding to user input) • Batch processing for data analysis (e.g., a scheduled anomaly detection job) • API integration with other systems (e.g., accessing a model via REST API) • Ensuring scalability (e.g., load balancing as user traffic grows) Deployment takes several forms: • Cloud deployment: Deploying on cloud platforms such as AWS, GCP, or Azure • Edge deployment: Running on smartphones or IoT devices (often combined with model compression) • On-premises deployment: Running models on a company's own internal servers After deployment, maintaining stable and safe model operation requires continuous model monitoring and integration with retraining pipelines. Representative deployment tools and platforms include TensorFlow Serving, TorchServe, MLflow, Kubeflow, SageMaker, and Vertex AI — all used as part of MLOps workflows. Model deployment is the "final step to making AI work in the real world" — the phase where its practical and business value becomes most visible.