Streamline your ML workflows with the right data at the right time

Unleash the Power of ML with Event-Native Data

Kurrent seamlessly connects real-time and historical data to your machine learning workflows, simplifying processes and maximizing efficiency

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Why Kurrent aligns perfectly with ML workflows

ML workflows depend on data that is accurate, complete, and, ideally, delivered with consistent meaning and format. 

Traditional data pipelines fall short by taking snapshots of data, introducing gaps and errors into data sets, and completely changing the meaning of data over time without passing on this context within the data sets they process.

Events provide better context to machine learning algorithms because they describe the effect (the event type) that a system command/workflow action had on a business object (state) in a very efficient and concise way.

Traditional data pipelines are lossy.

Kurrent changes the equation and makes your data pipelines lossless.

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Real-time and historical data

Capture every significant data point in real-time and preserve historical records for deep analysis.

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Minimal data prep

Deliver context-rich, structured events directly into your ML models without transformation.

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Out-of-the box interoperability with ML tooling

Geveraging the community-built Kurrent python client allows data scientists to directly stream events and replay streams of events to ML models without the need for ETL jobs

Streamline your ML workflows

It is widely acknowledged in the industry that interpolating, preparing, and cleansing data dominates the majority of time on ML projects, often consuming up to 95% of the overall effort to get to a useful model. The lower value preparation of data is time-consuming, yet necessary, contrasted to the high value of standing up models, training them, improving them and innovating.

Kurrent flips this dynamic

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Fewer transformations required

Events can flow directly into your models, maintaining context.

immediate-results

Immediate results

Get value from your data quickly accessed using fine-grained, indexed, streams.

simplified-architecture

Simplified architecture

Reduce or eliminate the need for complex ETL jobs, data preparation tooling, as well as imputation steps. Kurrent also provides built-in auditability of the entire span of each dataset.

The event-native advantage of Kurrent for ML

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Why traditional data models fall short

  • Lose critical historical data with each snapshot or update
  • Fail to retain context about how and why changes occurred
  • Require significant preprocessing, delaying insights
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How Kurrent powers your ML workflows

  • Granular context: Preserve every detail—“who,” “what,” “when” and “why”
  • Immutable records: Store events as immutable logs for compliance and traceability
  • Real-time streams: Deliver actionable data instantly to ML pipelines
  • Adaptable deployment: Operate seamlessly on-premises or in the cloud

Key technical benefits of Kurrent for ML workflows

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Novel data model

  1. Organize up to billions of fine grained streams to store the history of each entity
  2. Replay the history of each entity and update with latest data immediately
  3. Trust in guaranteed ordering across all streams
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Enterprise-grade security

  1. Immutable event storage ensures trust
  2. Fine-grained access control for sensitive data
  3. TLS encryption and full audit trails
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Operational flexibility

  1. Support for real-time and batch processing
  2. Deploy anywhere to optimize proximity to ML workloads
  3. Multi-language SDKs for diverse development needs
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Future-ready design

  1. Scalable architecture for growing workloads
  2. Easy adaptation to new ML technologies
Kurrent State of Your Business

Contextualizing the Kurrent State of Your Business

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Accelerate your ML workflows

Kurrent empowers your ML workflows by delivering event-native data directly to your models. 


This ensures:

  • Seamless access to high-fidelity data

  • Simplified infrastructure with fewer moving parts

  • Enhanced model performance with detailed context

  • Reduced time spent on data cleansing and interpolation of gaps in data sets

Elevate your ML with Kurrent

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