Manual ML Deployment

Manual ML deployment refers to the process of manually implementing, configuring, and managing the deployment of machine learning models into production environments without relying on automated deployment pipelines or specialized MLOps platforms. It involves tasks such as setting up servers, installing dependencies, writing custom scripts for model serving, and manually monitoring performance. This approach is often used in small-scale projects, proof-of-concepts, or environments where automation tools are not available or necessary.

Also known as: Manual Machine Learning Deployment, Hand-crafted ML Deployment, Custom ML Deployment, DIY ML Deployment, Traditional ML Deployment

Why learn Manual ML Deployment?

Developers should learn manual ML deployment when working on small projects, rapid prototyping, or in resource-constrained environments where setting up automated pipelines is overkill. It provides a foundational understanding of the deployment lifecycle, including model serialization, API creation, and infrastructure management, which is essential for troubleshooting and customizing deployments. Use cases include deploying simple models to cloud VMs, on-premises servers, or edge devices where full MLOps automation is not feasible.

Learning Resources

• tutorial: Deploying Machine Learning Models: A Step-by-Step Guide
• docs: MLOps: Machine Learning Operations
• course: Deploying ML Models with Flask and Docker
• video: Manual vs Automated ML Deployment: Pros and Cons
• book: Building Machine Learning Powered Applications