Model Deployment

Model deployment is the process of integrating a trained machine learning model into a production environment where it can serve predictions to end-users or other systems. It involves packaging the model, setting up infrastructure, and ensuring it operates reliably at scale. This step bridges the gap between model development and real-world application, enabling data-driven decision-making.

Also known as: MLOps Deployment, Productionizing Models, Model Serving, Deploying AI Models, Inference Deployment

Why learn Model Deployment?

Developers should learn model deployment to operationalize machine learning models, making them accessible for applications like recommendation systems, fraud detection, or automated customer service. It is essential for turning prototypes into impactful solutions, requiring skills in scalability, monitoring, and integration with existing software stacks to maintain performance and reliability in production.

Learning Resources

• docs: MLOps: Machine Learning Operations
• tutorial: Deploying Machine Learning Models with FastAPI
• course: Coursera: Deploying Machine Learning Models in Production
• video: YouTube: Model Deployment Explained
• book: Book: 'Building Machine Learning Powered Applications'