The term Model Deployment Pipeline originates from the fields of Artificial Intelligence, Automation, and Digital Transformation. A Model Deployment Pipeline describes all the steps necessary to successfully deploy a trained AI model in a real business environment – from development to everyday use.
Imagine the pipeline like a production line: a team of data experts develops an AI model, for example, one that automatically checks invoices. However, before this model is actually allowed to check invoices within the company, it must be safely tested, adapted, and integrated into the existing software. This is exactly what the Model Deployment Pipeline ensures. It automates and organises these processes, so that changes to the model or new versions can be integrated quickly and safely.
A clear example: An online shop wants to improve its product recommendations. The responsible AI model is trained and tested in a test environment. With the help of the Model Deployment Pipeline, the model is seamlessly integrated into the online shop, constantly monitored and automatically updated as needed. This way, both companies and customers directly benefit from better recommendations without IT departments having to intervene constantly.
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