MLOps for production-level machine learning

Presented by

Aaron Schneider

About this talk

MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Similar to the DevOps term in the software development world, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire ML lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics. In this webinar, we’ll discuss core practices in MLOps that will help data science teams scale to the enterprise level. You’ll learn the primary functions of MLOps, and what tasks are suggested to accelerate your teams machine learning pipeline. Join us in a discussion with Solutions Architect, Aaron Schneider, and learn how teams use MLOps for more productive machine learning workflows. * Reduce friction between science and engineering * Deploy your models to production faster * Health, diagnostics and governance of ML models * Kubernetes as a core platform for MLOps * Support advanced use-cases like continual learning with MLOps

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Learn from various data science and engineering experts about key topics for successful machine learning. will provide you with hands-on tutorials and workshops about the top methods in data science team management, and MLOps getting you from research to production. Stay ahead with the latest developments in auto-adaptive machine learning and CI/CD for machine learning. Learn the latest methods for machine learning model management and deployment with open source tools. Find answers on how to enhance team collaboration in your data science department, and smoothly bridge science to engineering.