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SageWorks: A Python WorkBench for creating and deploying AWS SageMaker Models

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  • License: MIT License (MIT)
  • Author: SuperCowPowers LLC
  • Tags SageMaker , Machine Learning , AWS , Python , Utilities
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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.

sageworks_new_light

Full AWS ML OverView

  • Health Monitoring 🟢
  • Dynamic Updates
  • High Level Summary

Drill-Down Views

  • Incoming Data
  • Glue Jobs
  • DataSources
  • FeatureSets
  • Models
  • Endpoints

Installation

  • pip install sageworks Installs SageWorks

  • sageworks Runs the SageWorks REPL/Initial Setup

For the full instructions for connecting your AWS Account see:

SageWorks Documentation

sageworks_api

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.

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