The term model deployment pipeline originates from the fields of artificial intelligence, automation and digital transformation. A model deployment pipeline describes all the steps required to successfully deploy a trained AI model in a real company - from development to everyday use.
Imagine the pipeline as a production line: A team of data experts develops an AI model that automatically checks invoices, for example. However, before this model can actually check invoices in the company, it must be securely tested, customised and integrated into the existing software. This is exactly what the Model Deployment Pipeline does. It automates and organises these processes so that changes to the model or new versions can be integrated quickly and securely.
An illustrative example: An online shop wants to improve its product recommendations. The relevant AI model is trained and tested in a test environment. With the help of the model deployment pipeline, the model is seamlessly imported into the online shop, constantly monitored and automatically updated if necessary. This means that companies and customers benefit directly from better recommendations without IT departments having to constantly intervene.
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