Metadata-Version: 2.4
Name: workbench-bridges
Version: 0.1.6
Summary: Workbench Bridges: End User Application Bridges to Workbench/AWS
Author-email: SuperCowPowers LLC <support@supercowpowers.com>
License: MIT
Project-URL: Homepage, https://github.com/SuperCowPowers/workbench-bridges
Keywords: SageMaker,Machine Learning,AWS,Python,Utilities
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: boto3>=1.28.76
Requires-Dist: botocore>=1.31.76
Requires-Dist: awswrangler>=3.4.0
Requires-Dist: sagemaker>=2.143
Requires-Dist: requests>=2.26.0
Dynamic: license-file

# Workbench Bridges
End User Application Bridges to Workbench/AWS ML Pipelines.

## Installation
```
pip install workbench-bridges
```

## Examples
Application invocation of an Endpoint on AWS.

```
import pandas as pd

# Workbench-Bridges Imports
from workbench_bridges.endpoints.fast_inference import fast_inference


if __name__ == "__main__":

    # Data will be passed in from the End-User Application
    eval_df = pd.read_csv("test_evaluation_data.csv")

    # Run inference on AWS Endpoint
    endpoint_name = "test-my-endpoint"
    results = fast_inference(endpoint_name, eval_df)

    # A Dataframe with Predictions is returned
    print(results)
```
