Metadata-Version: 2.1
Name: sageworks
Version: 0.4.31
Summary: SageWorks: A Python WorkBench for creating and deploying AWS SageMaker Models
Home-page: https://github.com/SuperCowPowers/sageworks
Author: SuperCowPowers LLC
Author-email: support@supercowpowers.com
License: MIT
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
Description-Content-Type: text/markdown
License-File: LICENSE
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Requires-Dist: numpy >=1.26.1
Requires-Dist: pandas >=2.1.2
Requires-Dist: scikit-learn >=1.4.1
Requires-Dist: awswrangler >=3.4.0
Requires-Dist: sagemaker >=2.143
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Requires-Dist: constructs >=10.3.0


# Welcome to SageWorks
The SageWorks framework makes AWS® both easier to use and more powerful. SageWorks handles all the details around updating and managing a complex set of AWS Services. With a simple-to-use Python API and a beautiful set of web interfaces, SageWorks makes creating AWS ML pipelines a snap. It also dramatically improves both the usability and visibility across the entire spectrum of services: Glue Job, Athena, Feature Store, Models, and Endpoints, SageWorks makes it easy to build production ready, AWS powered, machine learning pipelines.

<img align="right" width="500" alt="sageworks_new_light" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/ed2ed1bd-e2d8-49a1-b350-b2e19e2b7832">

### Full AWS ML OverView
- Health Monitoring 🟢
- Dynamic Updates
- High Level Summary

### Drill-Down Views
- Incoming Data
- Glue Jobs
- DataSources
- FeatureSets
- Models
- Endpoints


### Installation
- One time AWS Onboarding: [AWS Setup](https://supercowpowers.github.io/sageworks/aws_setup/core_stack/)
- After that your development team can simply `pip install sageworks`


### SageWorks Documentation
<img align="right" width="340" alt="sageworks_api" style="padding-left: 10px;"  src="https://github.com/SuperCowPowers/sageworks/assets/4806709/bf0e8591-75d4-44c1-be05-4bfdee4b7186">

[SageWorks Documentation](https://supercowpowers.github.io/sageworks/): The documentation contains examples from the SageWorks source code in this repository under the `examples/` directory. For a full code listing of any example please visit our [SageWorks Examples](https://github.com/SuperCowPowers/sageworks/blob/main/examples)


### SageWorks Beta Program
Using SageWorks will minimize the time and manpower needed to incorporate AWS ML into your organization. If your company would like to be a SageWorks Beta Tester, contact us at [sageworks@supercowpowers.com](mailto:sageworks@supercowpowers.com).

### Contributions
If you'd like to contribute to the SageWorks project, you're more than welcome. All contributions will fall under the existing project [license](https://github.com/SuperCowPowers/sageworks/blob/main/LICENSE). If you are interested in contributing or have questions please feel free to contact us at [sageworks@supercowpowers.com](mailto:sageworks@supercowpowers.com).

<img align="right" src="docs/images/scp.png" width="180">

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