The process of deploying trained ML models to production environments where they can receive inputs and return predictions. Model serving must handle scalability, latency, and reliability requirements.
The practice of combining Machine Learning, DevOps, and data engineering to streamline the deployment, monitoring, and maintenance of ML models in production. MLOps ensures reliable, scalable, and reproducible ML systems.
The process of using a trained model to make predictions on new data. Inference is distinct from training and typically requires optimization for speed and cost in production environments.
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