Model Serving

Model serving is the process of deploying machine learning models into production environments to make predictions on new data. It involves exposing trained models through APIs or services that can handle inference requests at scale, often with features like versioning, monitoring, and load balancing. This enables real-time or batch predictions for applications such as recommendation systems, fraud detection, and image recognition.

Also known as: ML Serving, Model Deployment, Inference Serving, MLOps Serving, Prediction Serving

Why learn Model Serving?

Developers should learn model serving to operationalize machine learning models, ensuring they deliver value in production by handling inference efficiently and reliably. It is crucial for building AI-powered applications that require low-latency predictions, scalability, and integration with existing systems, such as web services or mobile apps. Use cases include deploying models for chatbots, autonomous vehicles, or financial forecasting where timely and accurate predictions are essential.

Learning Resources

• docs: TensorFlow Serving Documentation
• course: MLOps: Machine Learning Operations on Coursera
• tutorial: Model Serving with FastAPI and Docker - Real Python Tutorial
• video: Introduction to Model Serving - YouTube Video by Data Science Dojo
• book: Building Machine Learning Powered Applications by Emmanuel Ameisen