Data Science Workbench
A reliable way to explore, experiment, and ship models—without losing track of data lineage, reproducibility, or cost.
- Experiments you can reproduce and compare
- Feature pipelines with versioning and drift alerts
- Seamless handoff from notebooks to production services
What’s in the Workbench
Tools that keep science scientific—so results are explainable, models are traceable, and handoffs are smooth.
Experiment Tracking
Track parameters, metrics, and artifacts. Compare runs, lock versions, and promote candidates with confidence.
Feature Store
Centralized, versioned features for online/offline parity. Built-in monitoring for drift and freshness.
Reproducible Pipelines
Deterministic data & model builds using containers and IaC. Consistent environments from dev to prod.
Model Registry
Stage, approve, and roll back versions with clear lineage and signatures. Automate promotions with checks.
Evaluation & Drift
Offline/online tests, canary rollouts, dashboards for performance & fairness, and alerts when data shifts.
Operational Handover
Package to services or jobs with consistent deploys. Integrate with CI/CD and your observability stack.
Workflow at a Glance
Keep the loop tight—from raw data to deployed model—so iteration speed stays high and quality stays consistent.
Ingest & Prepare
Profile, clean, and document sources. Version datasets to make results auditable and repeatable.
Experiment
Try ideas quickly. Log metrics and artifacts so you can compare apples to apples.
Package
Freeze environments, serialize models, and assemble pipelines so deploys are predictable.
Deploy & Monitor
Ship behind robust APIs or batch jobs. Watch performance, costs, drift—and roll back when needed.
Fits Your Stack
Designed for .NET teams with heterogeneous data science needs—interoperable with Python tools, schedulers, and your observability platform.
- Containers for portable compute
- Event & job orchestration patterns
- Clear boundaries for data and model ownership
Operational Confidence
Governance, auditability, and rollback are built in. Your models move forward—without losing the ability to go back.
- Signed artifacts & traceable lineage
- Guardrails for sensitive datasets
- Release gates for quality thresholds
Make your data science repeatable—and shippable
We’ll help your team set up a reproducible, auditable workflow—so good ideas reliably make it to production.
Talk to our team