Modern Monitoring and Diagnosis for Pipelines

Be the first to know the impact of every change in your pipelines and avoid costly data incidents.
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Are these your daily life?

Frustrated keeping track of your data changes on spreadsheets or paper.
Tired of working with multiple pipelines and datasets scattered everywhere.
Spending unnecessary time and resources on duplicated models.

PipeRider is made for you

End-to-end, Pipeline-wide visibility

PipeRider monitors changes across your pipeline and their impact to the outcome, notify you when they break data consumer's expectations, and helps you avoid repeating the same failure.

A Solid Foundation to support data-driven decisions

Use well-known data quality tests or custom tests that represent your domain insight to avoid data issues that will cause you 20%+ of revenue such as: data drift, problematic data selection and processing, and failure of data ingestion and transformation.

Collaboration and Knowledge Sharing

Reduce onboarding and maintenance cost for 50%+ by making sure everyone is always on the same page of data health with simple, code-based assertions that are integrated directly into your development process.

Work with any tools you love

By embracing modern data stack, PipeRider works with any pipeline at any scale. It can be gradually introduced to your stack with low integration cost. It's a true developer-first tool that grows with you.

A few more pains we heard from people...

Pain: Data versioning and tracking its impact across the whole pipeline

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Our Solution:
Several MLOps tools enable data and model versioning: DVC, Pachyderm, MLFLow, and Neptune. However, ML reproducibility means data + code + process. Integrating versioning schemes from multiple stages and tools becomes unmanageable quickly.

PipeRider uses simple, timestamp-based versioning to help you reason about the process and the quality of the outcome.

Pain: Visibility and Resilience of an ML system

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Our Solution:
Existing solutions often focus on the deployed service with no insight from the previous process. Traditional monitoring tools for operational metrics, such as Prometheus and Grafana, help us make sure the service is alive. Model quality monitoring tools, such as WhyLab, Comet, and Superwise give us insight on potential concept drifts.

PipeRider builds on top of existing, single-stage solutions and provides pipeline-wide visibility for developers to debug the cause of the drift and the impact radius. Such visibility enables better collaboration between scientists and developers and improves the speed of iteration.

Pain: Bridging the gap between real-time performance metrics and long-term business metrics.

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Our Solution:
The metadata storage should be capable of long-retention of data and allow out-of-order data ingestion. Complex versioning schema incurs limits on such capability.

PipeRider solves the problem by focusing on timestamp-based versioning and a tag filtering system. The simplicity of such schema makes it easier to get everyone onboard and lowers the entry barrier. Incorporating tools across the whole lifecycle becomes easier.

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Trusted by world-class ML practitioners

"It’s valuable to see when the data is collected, when the model is released, and when the data is changed."
Data Team Lead
"PipeRider would be helpful for us to fix the disconnect between experiment repo and deployment repos"
Data Scientists @ Insurance Industry