Video and Image ETL at Scale

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

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Breast Cancer Detection

In the example below we show how to create a scalable pipeline for breast cancer detection. 

There are different ways to scale inference pipelines with deep learning models. We implement two methods here with Pachyderm: data parallelism and task parallelism.

  • In data parallelism, we split the data, in our case breast exams, to be processed independently in separate processing jobs.
  • In task parallelism, we separate out the CPU-based preprocessing and GPU-related tasks, saving us cloud costs when scaling.

View the code on GitHub

Key Features of Pachyderm

Pachyderm is cost-effective at scale and enables data engineering teams to automate complex pipelines with sophisticated data transformations

Scalability

Deliver reliable results faster maximizes dev efficiency.

Automated diff-based data-driven pipelines.

Deduplication of data saves infrastructure costs.

Reproducibility

Immutable data lineage ensures compliance.

Data versioning of all data types and metadata. 

Familiar git-like structure of commits, branches, & repos.

Flexibility

Leverage existing infrastructure investment.

Language agnostic - use any language to process data 

Data agnostic - unstructured, structured, batch, & streaming

See Pachyderm In Action

Watch a short 5-minute demo which outlines the product in action

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