On-Demand Webinar

How to Use Feast Feature Store for Fintech?
[A Live Demo on Credit Scoring]

Understanding a feature store as a data management tool for machine learning, which allows users to share features and create robust ML pipelines, is important for data scientists and engineers.

Feature stores helps MLOps with better collaboration, faster development and deployment of models in production, better model accuracy, speeds up use case adoption, democratize ML, and more.

Join our webinar to learn what Feast is as an open-source feature store, how it serves features in production, operationalizes your analytics data, tracks and retrieves features for training and inference, and a live demonstration on how it helps the Fintech space in credit scoring cases.

Key insights will include
  • What is a feature store? How Feast helps?

  • How to setup Feast in an AWS environment?

  • What challenges do MLOps address?

  • How could Feast make a sustainable Feature Store infrastructure?

  • A live demo on real-time credit scoring via Feast

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    About the Speaker:

    Hassan Sherwani
    Hassan Sherwani

    Hassan Sherwani is the Head of Data Analytics and Data Science working at Royal Cyber. He holds a PhD in IT and data analytics and has acquired a decade worth experience in the IT industry, startups and Academia. Hassan is also obtaining hands-on experience in Machine (Deep) learning for energy, retail, banking, law, telecom, and automotive sectors as part of his professional development endeavors.

    Umer Qaisar
    Umer Qaiser

    Umer Qaisar is working as a Senior Data Scientist at Royal Cyber Inc.; he has experience delivering data-driven solutions on an enterprise scale in the financial industry. Umer has been involved in all the data science life cycle phases, from requirements understanding with business to development and deployment of use cases.

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