> ## Documentation Index
> Fetch the complete documentation index at: https://docs.superagentx.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Universal ML Handler

Modern AI systems often rely on multiple machine learning frameworks, each optimized for different tasks. The UniversalMLHandler
provides a unified, framework-agnostic interface for running machine learning inference across different ML ecosystems.

This handler dynamically routes inference requests to the appropriate framework-specific runner (such as Hugging Face, TensorFlow,
or scikit-learn) using an internal router. It abstracts away framework-specific initialization and execution details,
enabling agents and automation workflows to invoke ML models through a single, consistent API.

## Example

To run inference using the UniversalMLHandler, specify the framework, model configuration, and input data.
The handler automatically loads the required runner and executes inference.

```python theme={null}
from superagentx_handlers.ml.universal import UniversalMLHandler

ml_handler = UniversalMLHandler()
```

## Run Inference

**Run Hugging Face Model:** <br />
Executes a Hugging Face pipeline using a unified interface.

```python theme={null}
response = await ml_handler.run(
    framework="huggingface",
    task="text-classification",
    model_name="distilbert-base-uncased-finetuned-sst-2-english",
    inputs="This framework is incredibly flexible!",
    device="cpu"
)
print(response["output"])
```