Model Serving
Model serving is the process of deploying a trained machine learning model into a production environment so that it can receive new data and make predictions in real-time or batch. This involves packaging the model, setting up an API endpoint, and managing infrastructure to handle incoming requests efficiently and reliably. It's the final step that makes an AI model useful to end-users.
Model serving is like putting a finished product, say a calculator, on a store shelf after it's been designed and built, making it available for people to use.
Effective model serving is critical for realizing the value of AI models by making their predictive power accessible to applications and users in a scalable and performant manner.
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