Automating Production Level ML Operations on AWS

Presented by

Mark McQuade, Practice Manager, Data Science and Engineering & Daniel Quach, Lead Big Data Architect at Rackspace Technology

About this talk

Companies are realizing the value of using ML models to drive better outcomes for their businesses. Harnessing the predictive power of their data with ML models to remain competitive is becoming more critical to business operations, yet 60% of ML models never make it to production. This is due to complications often exacerbated by outdated business processes and the fact that it's not a core competency for typical data science teams. In this webinar, we will explore these specific challenges and illustrate how familiar cloud services can be stitched together with MLOps Foundations, enabling rapid development, training, scoring and deployment of models across multiple environments with strong governance and security controls to provide a foundation for successful model lifecycle management. What we'll cover: - Introduction to MLOps Foundations powered by Model Factory - The gap between the Data Scientists and ML Operations - The distinction between MLOps and DevOps - Architecture patterns necessary for elements of effective MLOps - How a “model factory” architecture holistically addresses CI/CD for ML Demo that will explore: - Quick feedback and traceability for model development - ML framework agnostic tooling for packaging of models - Platform agnostic continuous/rolling deployment
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Rackspace Technology is a leading end-to-end, hybrid, multicloud, and AI solutions company. We design, build, and operate our customers' cloud environments across all major technology platforms, irrespective of technology stack or deployment model. We partner with our customers at every stage of their cloud journey, enabling them to modernize applications, build new products and adopt innovative technologies. Learn more at