Integrated Machine Learning
Develop, train, track, and deploy models within a unified, governed analytics platform.
Notebook-native workflows powered by MLflow and built on the Groundzero lakehouse foundation.
Unified ML Matters
When machine learning operates outside the primary data and governance environment, model lineage fragments, metadata drifts, and operational complexity increases.
Groundzero integrates model development directly on the lakehouse foundation, ensuring data consistency, shared governance, and operational continuity.
Notebook-Native Development
Initiate Jupyter notebooks directly within the platform. Configure compute resources including GPU and memory allocation, and develop models in Python without external environment setup.
MLflow-Powered MLOps
Track experiments, manage model versions, and monitor training runs using open-source MLflow. Maintain transparency and avoid proprietary lock-in.
Scheduled Training and Automation
Schedule model training workflows within the same orchestration framework used for data engineering. Align retraining with data availability and business cycles.
Real-Time and Batch Predictions
Deploy models for batch inference or real-time predictions via APIs. Predictions can be consumed directly within Groundzero dashboards or external applications.
Built on the Lakehouse Foundation
Models train directly on governed datasets within the lakehouse. Metadata, access policies, and compute orchestration remain consistent across engineering, analytics, and ML workloads.
This eliminates the need for duplicated storage layers or isolated ML environments.
License Free Scalability
Compute resources scale based on actual usage, including GPU allocation when required. No per-model or per-user licensing limits are imposed on machine learning workloads.
By consolidating data engineering, ML, and analytics under a single operational framework, redundant infrastructure and tooling overhead are minimized.
