The modern data stack centered on Snowflake is bringing data driven business and operations increasingly within reach, yet an AI gap persists — 95% of leaders say their organization would benefit from embedding AI in business, but only 6% report adoption of AI across their organization*. Behind this AI gap are the many challenges associated with today’s ML solutions that require connecting a complex workflow across too many platforms, skill sets and tools. Numerous ML vendors are promising to breakthrough the complexity and democratize AI for every business, leaving buyers asking what solution is best, how much will it really cost, and whether it will really work for them. What if you could scale operational AI using the data stack, tools and skills you’ve already invested in?

Join us for this panel with thought leaders from Snowflake, Continual & dbt Labs where we’ll discuss:

• The growing demand for operational AI workloads on the modern data stack, including popular and emerging use-cases and deployment patterns, common challenges and success patterns
• Fluidity and evolution in data professionals’ skills and development practices, including the rise of analytics engineering: what it is and how it’s helping organizations realize being data driven
• How new, simplified approaches to operational ML on Snowflake are helping modern data teams deploy and scale production ML use-cases

Plus, you’ll see a live demo of how to deploy, maintain and scale continually improving predictions using GitOps-friendly dbt workflows in Continual on Snowflake.