ML System for Embedded Lending

ML System for Embedded Lending

The Challenge:

Modernization and improvement of Machine Learning infrastructure.


  • There wasn’t enough visibility and monitoring of production predictions of ML models.
  • No infrastructure for A/B testing ML models and parameters in production.
  • The ML model deployment process was neither standardized nor well documented

The Engagement:

Novacomp provided a Staff Augmentation service to the Machine Learning and Data Science team.

  • Novacomp team integrated into the company native team, participating in each of their stand-ups, Sprint planning, and sync meetings.
  • ML team collaborated with other Novacomp staff-augmented teams like Data Engineering and Analytics.
  • Novacomp team brought expertise in ML ops technologies and best practices, helping design and implement a new state of the art ML architecture using FastAPI,, Weights and Biases, Docker, and Apache Pulsar.

The Benefit:

    • Implemented a micro service ML architecture that allowed parallel deployments of models for AB testing purposes.
    • Developed an Event-driven monitoring system for ML predictions using Apache Pulsar.
    • Documented the deployment process, allowing the Data Science team to deploy changes on their own.
    • Improved ML services latency by refactoring old code and making asynchronous calls.

    Key Technologies:





    Scikit Learn



    Weights & Biases

    Apache Pulsar

    Data Feeds / Integrations

    Feeds from Python and Java Microservices Architecture, Snowflake Datawarehouse, and Event-based Streaming with Apache Pulsar.

    Integrates into a Sigma Dashboard built by the analytics team.

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