Manual Model Deployment

Manual Model Deployment is a process in machine learning operations (MLOps) where data scientists or engineers manually handle the steps to deploy a trained machine learning model into a production environment. This typically involves tasks like packaging the model, configuring infrastructure, setting up APIs, and managing dependencies without extensive automation. It contrasts with automated deployment pipelines, requiring hands-on intervention for each deployment cycle.

Also known as: Manual ML Deployment, Hands-on Model Deployment, Non-automated Deployment, Manual MLOps, Custom Model Deployment

Why learn Manual Model Deployment?

Developers should learn manual model deployment when working in small-scale projects, prototyping, or environments where automation tools are not yet implemented, as it provides foundational understanding of deployment workflows. It is useful for scenarios requiring custom configurations, quick iterations, or when deploying models to edge devices with specific constraints. However, for large-scale, frequent deployments, automated MLOps practices are recommended to improve efficiency and reliability.

Learning Resources

• tutorial: Deploying Machine Learning Models: A Step-by-Step Guide
• docs: MLOps: Machine Learning Operations
• video: Deploying ML Models with Flask and Docker
• book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• course: Model Deployment in Production