SageWorks up on the AWS Marketplace
Powered by AWS® to accelerate your Machine Learning Pipelines development with our new Dashboard for ML Pipelines. Getting started with SageWorks is a snap and can be billed through AWS.
Road Map: v0.9.0
We've used the feedback from our current beta testers to improve the framework and we've constructed a mini road map for the upcoming SageWorks version 0.9.0. Please see SageWorks RoadMaps
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.
- Health Monitoring 🟢
- Dynamic Updates
- High Level Summary
- Incoming Data
- Glue Jobs
- DataSources
- FeatureSets
- Models
- Endpoints
Secure your Data, Empower your ML Pipelines
SageWorks is architected as a Private SaaS (also called BYOC: Bring Your Own Cloud). 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. By using SageWorks, you maintain absolute control over your data while benefiting from the power, security, and scalability of AWS cloud services. SageWorks Private SaaS Architecture
-
pip install sageworksInstalls SageWorks -
sageworksRuns the SageWorks REPL/Initial Setup
For the full instructions for connecting your AWS Account see:
- Getting Started: Initial Setup
- One time AWS Onboarding: AWS Setup
Even though SageWorks makes AWS easier, it's taking something very complex (the full set of AWS ML Pipelines/Services) and making it less complex. SageWorks has a depth and breadth of functionality so we've provided higher level conceptual documentation See: SageWorks Presentations
The SageWorks documentation SageWorks Docs covers the Python API in depth and contains code examples. The documentation is fully searchable and fairly comprehensive.
The code examples are provided in the Github repo examples/ directory. For a full code listing of any example please visit our SageWorks Examples
The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or chat us up on Discord
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.
pip install sageworks # Installs SageWorks with Core Dependencies
pip install 'sageworks[ml-tools]' # + Shap and NetworkX
pip install 'sageworks[chem]' # + RDKIT and Mordred (community)
pip install 'sageworks[ui]' # + Plotly/Dash
pip install 'sageworks[dev]' # + Pytest/flake8/black
pip install 'sageworks[all]' # + All the things :)
*Note: Shells may interpret square brackets as globs, so the quotes are needed
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