Yochay Ettun and Chris Banyai: Using MLOps to Build Scalable End-to-End AI Pipelines
May 11, 2021
About this Video
Prior to the existence of MLOps platforms, the manual setup and configuration of even a small-scale version of an intended end-to-end AI pipeline was often cumbersome, complex, and time consuming. MLOps allows you to more quickly and easily construct and deploy versioned end-to-end AI pipelines composed from individual stages or building blocks, with control over where each stage runs (on-premises or in the cloud). Yochay Ettun and Chris Banyai take you through rapidly constructing, deploying, modifying, and scaling an end-to-end pipeline using the cnvrg.io platform. Join Cnvrg.io's Yochay Ettun and Intel's Chris Banyai to discover how MLOps can enable your data science team in the research phase, the training and inference stages, and beyond.