Disappearing MLOps: Seamless integration of Striveworks' Chariot and AWS

This webinar explores the challenge of maintaining an efficient feedback loop for updating machine learning (ML) models, which is often perceived as laborious and challenging. We will discuss the integration of machine learning operations (MLOps) technology with Amazon Web Services (AWS) data systems, known as "disappearing" MLOps. By seamlessly integrating with the Striveworks Chariot platform, AWS customers can now leverage existing production data systems to achieve further scalability and operational improvement.

AWS offers a comprehensive suite of extract, transform, and load (ETL) tools for effortless integration. The increasing demand for foundational artificial intelligence (AI) and ML models, coupled with customized retraining and optimization for client-specific applications, necessitates this shift.

Join us to discover how the Striveworks Chariot platform, combined with AWS, allows data science teams to seamlessly integrate model retraining and monitoring into existing production systems, eliminating the slowdowns caused by ongoing updates and maintenance in the model development cycle. We will also delve into how this approach enables robust, enterprise-scale data governance and privacy.

Learning objectives

• Explore an innovative, integrated, and automated business model for MLOps.
• Understand the benefits of deep integration between the MLOps process and production data systems.
• Discover the distinctive capabilities offered by Striveworks to empower data analytic teams.