> For the complete documentation index, see [llms.txt](https://docs.bdb.ai/data-pipeline-2/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bdb.ai/data-pipeline-2/data-pipeline/ml-and-data-ops.md).

# ML and Data Ops

### ML Ops <a href="#ml-ops" id="ml-ops"></a>

BDB Data Pipeline allows you to operationalize your AI/ML Models in few minutes. The Models can be attached to any pipeline to get the inferences in real-time. Then the inferences can either be used in any other process or get shared with the user instantly.

<figure><img src="https://859511478-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FGDmsjfjJBNqow7Fo97cO%2Fuploads%2FGpKV7eA5Pg9i93iTau0D%2Fimage.png?alt=media&#x26;token=2bd73d38-410a-48a9-a078-5e58ce555b71" alt=""><figcaption></figcaption></figure>

[Data Science Lab Model & Script Runner](https://docs.bdb.ai/7.6/data-pipeline/components/ai-ml/dsl-model-and-script-runner)

### Data Ops <a href="#data-ops" id="data-ops"></a>

Traditional data transformation operation are sequential process where developer design and develop the logic and test and deploy it. BDB Data Pipeline allows the user to adopt the agile non-linear approach which reduces the time to market by 50 to 60 %​

<figure><img src="https://859511478-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FGDmsjfjJBNqow7Fo97cO%2Fuploads%2F8Qu8X18iFTSaipx9AczC%2Fimage.png?alt=media&#x26;token=94226ebe-d4fa-4d0a-a1af-4f5baaa10ec1" alt=""><figcaption></figcaption></figure>


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.bdb.ai/data-pipeline-2/data-pipeline/ml-and-data-ops.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.