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SageWorks: Dashboard for AWS ML Pipelines

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 Jobs, Athena, Feature Store, Models, and Endpoints. SageWorks makes it easy to build production ready, AWS powered, machine learning pipelines.

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About

SageWorks is architected as a Private SaaS. This hybrid architecture is the ultimate solution for businesses that prioritize data control and security. SageWorks deploys as an AWS Stack within your own cloud environment, ensuring compliance with stringent corporate and regulatory standards. It offers the flexibility to tailor solutions to your specific business needs through our comprehensive plugin support, both components and full web interfaces. By using SageWorks, you maintain absolute control over your data while benefiting from the power, security, and scalability of AWS cloud services.

The SageWorks solution has two main components, a Web Interface that provides visibility into AWS ML PIpelines and a Python API that makes creation and usage of the AWS ML Services easier than using/learning the services directly. The SageWorks Dashboard has a set of web interfaces that give visibility into the AWS Glue and SageMaker Services. There are currently 5 web interfaces available:

Top Level Dashboard: Shows all AWS ML Artifacts (Glue and SageMaker)

DataSources: DataSource Column Details, Distributions and Correlations

FeatureSets: FeatureSet Details, Distributions and Correlations

Model: Model details, performance metric, and inference plots

Endpoints: Endpoint details, realtime charts of endpoint performance and latency

The SageWorks Python API is used is to encapsulate and manage a set of AWS services underneath a Python Object interface. The Python Classes are used to create and interact with Machine Learning Pipeline Artifacts.

For more info on the API see our SageWorks Documentation Pages: https://supercowpowers.github.io/sageworks/

For custom pricing, EULA, or a private contract, please contact aws-marketplace@supercowpowers.com, for a private offer.

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