Video and Image ETL at Scale with Pachyderm

Video and imaging ETL is characterized by large unstructured data sets that can create bottlenecks for teams as they look to productionize and scale.

Pachyderm’s data layer provides petabyte scalability through a purpose built data versioning system for unstructured data, while Pachyderm’s pipelines are optimized for incremental and parallel data processing. This speeds data tasks across the entire ML lifecycle from preparation to experimentation and training, and finally to deployment.

Read our article on Scaling Breast Cancer Detection and try the example for free on our hosted service Pachyderm Hub.

Image Detection - Try our Example!

  1. Sign-up for a free account to try the example on Pachyderm Hub
  2. Example documentation, data and code on GitHub

Scaling Breast Cancer Detection with Pachyderm

Pachyderm helps you to scale research to get it into practitioners' hands faster.

  • Data Driven Automation

    Automate your MLOps tool chain with data driven pipelines and data versioning.

    • Automatically trigger pipelines when new data arrives
    • Ability to process only new or changed data
    • Code agnostic - supports any library or language
  • Petabyte Scalability

    Rapidly process the largest unstructured and structured data sets

    • Parallel processing that requires no code changes
    • Scalable data versioning optimized to lower storage and compute costs
    • Kubernetes native
  • End-to-End Reproducibility

    Ensure reproducibility with automatic data versioning and immutable lineage

    • Faster data debugging
    • Ideal for meeting data governance requirements
    • Ease compliance and audit tasks