Feature Store as a Data Foundation for Machine Learning

Learn how to build a centralized, scalable Feature Store for Machine Learning, to drive innovation at scale

On-Demand Webinar

Feature Store is a key component of the ML stack and data infrastructure. By enabling robust feature engineering and management, it helps organizations save massive amounts of resources, innovate faster, and drive ML processes at scale. Request the webinar and learn how to build a scalable Feature Store with a data mesh pattern; see how to achieve consistency between real-time and training features, to improve reproducibility with time-traveling for data.

You will learn about:

  • Modern Data Lakes and Modern ML Infrastructure
  • Existing and Emerging Architectural Shifts
  • Feature Store: Overview and Reference Architecture
  • AWS Perspective on Feature Store
  • Provectus ML Infrastructure Acceleration Program

Speakers:

  • Stepan Pushkarev, Chief Technology Officer, Provectus
  • Gandhi Raketla, Senior Solutions Architect, AWS
  • German Osin, Senior Solutions Architect, Provectus

Who should attend:

  • Technology executives & decision makers
  • Manager-level tech roles
  • Data architects & analysts
  • Data engineers & Data scientists
  • ML practitioners & ML engineers
  • Developers

Let’s explore major use cases and ways to build a centralized, scalable Feature Store for Machine Learning!

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Feature Store as a Data Foundation for Machine Learning

Learn how to build a centralized, scalable Feature Store for Machine Learning, to drive innovation at scale

On-Demand Webinar

Feature Store is a key component of the ML stack and data infrastructure. By enabling robust feature engineering and management, it helps organizations save massive amounts of resources, innovate faster, and drive ML processes at scale. Request the webinar and learn how to build a scalable Feature Store with a data mesh pattern; see how to achieve consistency between real-time and training features, to improve reproducibility with time-traveling for data.

You will learn about:

  • Modern Data Lakes and Modern ML Infrastructure
  • Existing and Emerging Architectural Shifts
  • Feature Store: Overview and Reference Architecture
  • AWS Perspective on Feature Store
  • Provectus ML Infrastructure Acceleration Program

Speakers:

  • Stepan Pushkarev, Chief Technology Officer, Provectus
  • Gandhi Raketla, Senior Solutions Architect, AWS
  • German Osin, Senior Solutions Architect, Provectus

Who should attend:

  • Technology executives & decision makers
  • Manager-level tech roles
  • Data architects & analysts
  • Data engineers & Data scientists
  • ML practitioners & ML engineers
  • Developers

Let’s explore major use cases and ways to build a centralized, scalable Feature Store for Machine Learning!