“Good Enough” Data Isn’t AI-Ready. Here’s What to Fix.

Replace manual pipelines with AI-ready data delivery

Snowflake gives you a powerful data platform, but it doesn’t solve CI/CD, testing, or governance out of the box. Most teams fill that gap with custom scripts, stitched tooling, and manual processes that don’t scale.

In this session, see how to eliminate that complexity and build automated, production-ready data pipelines for AI.

What you’ll learn:

• AI-ready data scoring
  Continuously measure and validate AI data readiness so you know your data is fit for production AI use cases.
• Replace manual CI/CD with pipeline automation
  Eliminate brittle, script-based workflows and automate build, test, and deployment across Snowflake environments.
• Continuous data observability
  Get real-time visibility into data quality for AI, lineage, and pipeline performance so issues are caught before they impact AI outcomes.
• Embedded governance and compliance
  Enforce policies directly within pipelines so governance is automatic, not an afterthought.
• Metis: Data Engineering AI agent
  Automate repetitive tasks like code generation, documentation, and pipeline setup, freeing engineers to focus on higher-value work.
  

Who should watch

• Data engineers modernizing data pipeline automation and CI/CD practices
• Analytics engineers building scalable, reliable data products for AI use cases
• Data platform leaders responsible for delivering AI-ready data across the organization
• Data governance and quality teams improving data quality, trust, and compliance
• Snowflake users looking to operationalize data for AI within their platform