Pachyderm provides industry leading data versioning, pipelines and lineage that allow data science teams across a variety of industries and use cases to automate the machine learning lifecycle and optimize their machine learning operations (MLOps).

Whether focused on financial services, life sciences/biotech, natural language processing (NLP), or video and image ETL, Pachyderm provides MLOps teams a robust solution accelerator that allows them to productionize their ML life cycle with a data foundation resulting in quick delivery, while lowering cloud costs and meeting data governance requirements.

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Meet Your MLOps Needs

Pachyderm is the solution accelerator that allows data science teams to meet their MLOps needs across a number of industries, use cases, and data types (video, images, unstructured text, etc) and plays a leading role in AIIA and defining reference architectures for the MLOps solutions.

  • Industries

    • Pachyderm for Biotech & Life Sciences

      The scientific method’s reproducibility gave birth to the scientific revolution. As biotech firms turn to AI/ML to drive next generation drug discovery, they need a new kind of reproducibility: Pachyderm’s data lineage delivers the scientific method data scientists need to create scalable, repeatable experiments now.

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    • Pachyderm in Financial Services

      Machine Learning has made a large impact in Financial Services by addressing a wide range of applications from fraud detection to improved customer service and robo investing tools. Learn how Pachyderm’s Automated Data Versioning and Data-Driven Pipelines help data science teams in Financial Service organizations to automate and scale their machine learning lifecycle while guaranteeing reproducibility.

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  • Use Cases

    • Natural Language Processing (NLP) with Pachyderm

      Natural Language Processing (NLP) can benefit greatly from Pachyderm’s automated versioning and data driven pipelines.

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    • 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.

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