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 team’s machine learning pipeline.
Join us in a discussion with Vinay Kumar, DevOps Engineer at Knoldus Inc., and learn how teams use MLOps for more productive machine learning workflows.
Vinay has been connected to Knoldus for 2.5+ years as a Software Consultant focussing on the four pillars like Orchestration, Containerization, CI/CD and Unix systems. I experienced orchestration using DC/OS, Kubernetes and Docker Swarm and have explored Docker and the upcoming niche of Rkt (rocket). I've got certified in Kubernetes Administration (CKA) and believe DevOps is a philosophy more than just set of tools.