Welcome to the Spring 2022 Edition of the Modern Data Stack Ecosystem. In this article, we’ll provide an in-depth look at the Modern Data Stack (MDS) ecosystem, updated from our Fall 2021 edition. We also highly recommended our article, The Future of the Modern Data Stack, to anyone who is new to the MDS and wants to learn about its history.

Data infrastructure is rapidly growing and evolving along with infrastructure for AI/ML, with the latter growing largely independent from the former. An emerging generation of AI/ML tooling emphasizes data-centric versus model-centric approaches to the ML development lifecycle. These tools recognize that data is the foundation for AI and seek to open opportunities for all data professionals to participate by eliminating the unnecessary complexity of traditional model-centric solutions.

This guide will show you how to easily add Continual as the AI layer to your modern data stack with Snowflake at the core. The intention is to provide an introduction to using Continual on Snowflake. After completing this tutorial, users are invited to try more advanced examples. We are going to demonstrate connecting Continual to Snowflake, building feature sets and models from data stored in Snowflake, and analyzing and maintaining the predictive model continuously over time.