SageWorks: A Python WorkBench for creating and deploying AWS SageMaker Models
Project description
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.
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
- After that your development team can simply
pip install sageworks
SageWorks Documentation
SageWorks Documentation: 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
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.
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. If you are interested in contributing or have questions please feel free to contact us at sageworks@supercowpowers.com.
® Amazon Web Services, AWS, the Powered by AWS logo, are trademarks of Amazon.com, Inc. or its affiliates.
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