what is deployment in data science

what is deployment in data science
In data science, deployment means taking a trained model, data pipeline, or analysis solution and integrating it into a real-world system so it can be used to make predictions, automate decisions, or provide insights in everyday operations.

In simple words:
It’s the step where your data science work moves out of your notebook or local machine and becomes part of an actual product, website, app, or business process.

For example:

Deploying a machine learning model that predicts customer churn into a company’s CRM system so sales teams can act on it.

Integrating a recommendation engine into an e-commerce site to suggest products to users.

Setting up an automated data dashboard that updates daily for business managers.

Deployment makes data science useful and actionable — turning analysis and models into tools that people or systems can use automatically, reliably, and often in real time.

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