Model deployment utilizes APIs to enable data-based decisions. APIs, or Application Programming Interfaces, serve as a bridge between the deployed machine learning model and other applications or systems. They facilitate communication, allowing various software components to interact with one another.

When a model is deployed, it can be accessed via an API endpoint, which enables other applications to send data to the model and receive predictions or insights in return. This setup allows organizations to integrate machine learning capabilities into their existing workflows seamlessly.

For example, a web application can use an API to send user data to a deployed model, receive a prediction, and then take action based on that prediction, such as recommending a product or sending alerts.

### Final Answer
**APIs** enable data-based decisions in model deployment.

Question

Model deployment utilizes APIs to enable data-based decisions. APIs, or Application Programming Interfaces, serve as a bridge between the deployed machine learning model and other applications or systems. They facilitate communication, allowing various software components to interact with one another.

When a model is deployed, it can be accessed via an API endpoint, which enables other applications to send data to the model and receive predictions or insights in return. This setup allows organizations to integrate machine learning capabilities into their existing workflows seamlessly.

For example, a web application can use an API to send user data to a deployed model, receive a prediction, and then take action based on that prediction, such as recommending a product or sending alerts.

### Final Answer
**APIs** enable data-based decisions in model deployment.

Knowee AI · Accepted Answer

Model deployment utilizes APIs to enable data-based decisions. APIs, or Application Programming Interfaces, serve as a bridge between the deployed machine learning model and other applications or systems. They facilitate communication, allowing various software components to interact with one another.

When a model is deployed, it can be accessed via an API endpoint, which enables other applications to send data to the model and receive predictions or insights in return. This setup allows organizations to integrate machine learning capabilities into their existing workflows seamlessly.

For example, a web application can use an API to send user data to a deployed model, receive a prediction, and then take action based on that prediction, such as recommending a product or sending alerts.

### Final Answer
**APIs** enable data-based decisions in model deployment.

What does model deployment use to enable data-based decisions?1 pointGitlabAPIsPackagesWeb Applications

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What does model deployment use to enable data-based decisions?

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