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Deployment

feature · part of AI ML Engineering Workflow

Deployment integrates the trained model into a real-world application, such as a web service, mobile app, or automated system. Platforms like AWS, Microsoft Azure, or Google Cloud AI are commonly used to deploy models at scale. Deployment is a critical step for moving from development to production.

AI/ML engineers deploy models into production environments, integrating them into existing software or cloud infrastructure. Deployment ensures models handle real-time data and user interactions efficiently. Scalability is a key consideration, requiring understanding of software engineering and infrastructure management.

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