Industry Leaders Choose 4Paradigm

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  • Low barrier to use

    Based on Kolb’s Learning Cycle, enable a four-step methodology for building AI application, covering the entire machine learning process from modeling to application.

  • Higher adoption efficiency

    Provide leading automated modeling, real-time go-live and closed-loop data to facilitate algorithm engineering and improve the efficiency in AI adoption.

  • Continuously improved effectiveness

    Based on the advanced home-grown AutoML technology and HyperCycle methodology, enable self-learning with real-time closed-loop data to ensure and continuously improve model effects.

  • Cost-effective AI adoption

    Provide automated AI adoption services across the full process, making it possible to build AI apps at an affordable cost, while ensuring the same project quality.


  • User-friendly interface

    With simple configuration, users can easily launch a machine learning cycle to support continuous learning and estimation, making AI products truly accessible to business users.

  • Fully automated modeling

    Integrate AutoML capabilities across the full process to automatically support data processing, table splicing, feature derivation, feature selection, algorithm optimization and model selection. Simplify machine learning process into data preparation for business users with automatic table splicing.

    With the world-leading and self-developed AutoML technology, outperform 85% of data scientists in many competition datasets and effectively ensure business outcomes.

  • Flexible and convenient application

    Enable automated model deployment, real-time/batch processing models, one-click online/offline application and multi-dimensional reports.

    Support model exploration, batch estimation, and automatic retry of the failed model self-learning tasks.

  • Iterative self-learning improvement

    Enable iterative self-learning from actual feedbacks based on closed-loop approach, while providing full self-learning and incremental self-learning to iteratively improve the model and ensure the long-term effectiveness of the model.

    Update the model in minutes powered by incremental self-learning, maintaining the flexibility to address the ever-changing business needs.

Business Scenarios

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    Precise marketing

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    Intelligent recommendation

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    Sales forecast

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    Churn prediction

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    Default risk prediction

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    Anti-money laundering

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    Failure prediction