Machine Learning Engineering
Deployment refers to the process of making a machine learning model available for use in a production environment, allowing it to serve predictions or insights based on new data. This process includes various tasks such as preparing the environment, ensuring scalability, and integrating with other systems. Effective deployment is crucial for transforming a trained model into an operational tool that delivers real-world value.
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