Accelerate ML Experiments with Enterprise-Grade Data Version Control
Collaborate, iterate, and reproduce experiments without data duplication.
Efficiently run ML experiments
Transform how your ML teams experiment with data by enabling collaboration, reproducible results, and efficient iterative experimentation.
Setup experimentation environments in a single operation
- Create data branches for
isolated experiment environments
in milliseconds – not hours - Run parallel experiments without
duplicating terabytes of data - Work locally on production data
without toggling between tools
Reproduce experiments
with confidence
- Anchor experiments to immutable
data versions - Track datasets’ evolution alongside model
changes - Version both code and data together
with Git integration
Collaborate on datasets at scale
- Create isolated experiment environments
- Share and reuse datasets securely across
teams - Promote successful experiments to
production via pull requests
Supercharge existing
ML tooling
- Connect seamlessly with MLflow or
any other experiment trackers - Add scalable data versioning to any ML
tracking system - Unify experiment and data versioning in one
workflow
Simplify data curation
- Curate experimental datasets using metadata filtering
- Search and discover datasets based on rich metadata
- Track full dataset lineage: from origin to evolution
Transparent, traceable and repeatable development of AI is critical to us.
What's important for Lockheed Martin is that we don't just focus on what we're building but also on the how.
Greg ForrestDirector of AI Foundations
You have a centralized system for a decentralized organization structure, which is highly beneficial. lakeFS provides a centralized solution and toolchain for ML and data science 
With lakeFS, we have streamlined data science and MLOps workflows, adapted data access controls for different data teams, accelerated productivity and reduced time-to-insights for ML engineering projects 
