Rapidly productionize and scale your machine learning lifecycle

  • Automated Data Versioning

    Pachyderm’s Data Versioning gives teams an automated and performant way to keep track of all data changes

    • Utilizes a Git-like structure that enables effective team collaboration through commits, branches and rollbacks
    • Optimized storage framework supports petabytes of structured and unstructured data, while minimizing storage costs
    • File-based versioning provides a complete audit trail for all data and artifacts across pipeline stages, including intermediate results
    • Stored as native objects (not metadata pointers) so that versioning is automated and guaranteed
    Try for Free
  • Data-Driven Pipelines

    Pachyderm’s Containerized Pipelines speed data processing while lowering compute costs

    • Kubernetes native approach supports any library or language
    • Autoscale with parallel processing of data without writing additional code
    • Automated pipelines execute whenever new data is committed
    • Incremental processing saves compute by only processing differences and automatically skipping duplicate data
    • Pipeline steps have JSON/YAML defined inputs and outputs that ease debugging
    Try for Free
  • Immutable Data Lineage

    Pachyderm’s data lineage provides an immutable record for all activities and assets in the ML lifecycle:

    • Track every version of your code, models, and data
    • Maintain reproducibility of data and code for compliance
    • Manage relationships between historical data states

    Pachyderm’s Global IDs make it easy for teams to track any result all the way back to its raw input, including all analysis, parameters, code, and intermediate results.

    Try for Free
  • Console

    The Pachyderm Console provides an intuitive visualization of your DAG (directed acyclic graph), and aids in reproducibility with Global IDs

    • See the overall structure and flow of all your pipelines
    • Ease pipeline and workflow design
    • Facilitate collaboration across teams on shared DAGs
    • Drill into pipelines and job details for easy debugging
    Try for Free
  • Notebooks

    Pachyderm Notebooks provide an easy way to interact with Pachyderm data versioning and pipelines via Jupyter notebooks

    • Unify data engineering and data science for better collaboration
    • Create and mount data repos for easy access to large and changing data sets
    • Use data repos and pipelines to iterate rapidly on experiments and training, while maintaining reproducibility
    • Easily debug pipelines
    Try for Free

See these features in action for free on Hub, or request a demo from our account team!