STACKIT Notebooks provides a managed JupyterLab environment for data scientists and ML engineers. Integration with STACKIT Object Storage, AI Model Serving, and PostgreSQL Flex enables a complete ML workflow on sovereign GDPR-compliant infrastructure.
Features
- JupyterLab Environment: Full JupyterLab interface with extension support
- Pre-configured Kernels: Python, R, and other languages with pre-installed data science libraries
- GPU Support: Optional GPU instances for deep learning workloads
- Persistent Storage: Notebooks and data persist between sessions
- STACKIT Integration: Direct access to Object Storage, PostgreSQL Flex, and other services
Typical Use Cases
Data Exploration: Data scientists interactively explore datasets from STACKIT Object Storage or PostgreSQL Flex before automating pipelines in STACKIT Workflows.
Model Development: ML engineers develop and test models in notebooks, track experiments via AI Model Experiments, and deploy finished models via AI Model Serving.
Benefits
- GDPR-compliant: All notebooks and data in German data centers
- No Setup: Ready to use immediately without local installation
- Complete ML Stack: Integration with the entire STACKIT data and AI ecosystem
- Collaborative: Shared team access to notebooks
Integration with innFactory
As an official STACKIT partner, innFactory supports you in building data science environments: from notebook configuration and data service integration to production MLOps workflows.
