Dataka
Dataka
Reproducible Jupyter Notebook Publishing
Published notebooks rerun in the cloud with stable, versioned links to output. All your business logic and data lineage are documented in one single source of truth.
Reproducibility in 3 Steps
1. Publish your notebooks via the command line or drag-and-drop
Easy to learn: use drag-and-drop or good-old command prompt to upload. Published notebooks are executed in the cloud and versioned.
1. Upload Files
Drag or Drop Here
2. Share notebooks as stable links safeguarded by enterprise-grade security
Say goodbye to searching through emails for attachments. Expand your work's impact with easily sharable notebook links. Secure your IP with intuitive fine-grained permissions and single sign-on.
Share User_Churn.ipynb
3. Download and re-run locally as auto-generated reproducible containers
Effortlessly reproduce a notebook's development environment. Update analyses as new versions to build on prior work instead of reinventing the wheel.
Advanced Features
Extensible Environment
Install custom libraries (requirements.txt) or redefine your environment (Dockerfile). Just include the file and we’ll do the rest.
Coding Style
Automatic suggestions for improving code style, quality, and readability.
Other Languages
Support for R and Julia coming soon
About Us
There are many tools claiming to simplify dev-ops for data scientists. Most have steep learning curves and are too complex for data science use cases. Our tools are built by data scientists for data scientists. They are powerful but intuitive to learn.
Other Products
Deploy ML Models as Documented REST APIs
Turn any python prediction function into a REST API, deployed on the cloud. API documentation and client libraries are inferred from type annotations and docstrings and automatically published to the web.
Office
Dataka Office
415 Mission St
26th Floor
San Francisco, CA 94105
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