In this session, we will describe the challenges in operationalizing machine & deep learning. We’ll explain the production-first approach to MLOps pipelines - using a modular strategy, where the different components provide a continuous, automated, and far simpler way to move from research and development to scalable production pipelines. Without the need to refactor code, add glue logic, and spend significant efforts on data and ML engineering. We will cover various real-world implementations and examples, and discuss the different stages, including automating feature creation using a feature store, building CI/CD automation for models and apps, deploying real-time application pipelines, observing the model and application results, creating a feedback loop and re-training with fresh data.

We’ll demonstrate how to use Iguazio & Snowflake to create a simple, seamless, and automated path to production at scale!