Pino Suliman

To effectively govern your AI models end to end, you need visibility and controls across the entire data lifecycle. This includes ensuring data quality for model inputs, using data lineage to track dependencies and workflows, and enforcing safeguards on model outputs. Join this session to learn best practices for governing models with Snowflake — improving data integrity, increasing transparency into your AI pipeline, and ensuring responsible AI deployment.

Ensuring the reliability and accuracy of your data is paramount. This session will explore how Snowflake empowers organizations to proactively monitor and manage data quality at scale. Learn how to automate quality checks, validate data freshness, and run custom rules across your data pipelines. We’ll share real-world examples, best practices, and new Snowflake features that streamline data quality monitoring, helping you build trust in your data and drive confident decision making. This session is perfect for data engineers, analysts and data stewards looking to elevate their data governance strategies.

As your data grows and becomes spread across tens or even hundreds of accounts, you might be in search of a tool that makes it easier to manage your storage footprint and improve cost efficiencies.

As your data grows and becomes spread across tens or even hundreds of accounts, you might be in search of a tool that makes it easier to manage your storage footprint and improve cost efficiencies. Amazon S3 Storage Lens is an analytics feature built into the S3 console to help you gain organization-wide visibility into your object storage usage and to identify cost savings and data protection...

If you have an increasing number of Amazon S3 buckets spread across tens or even hundreds of accounts, you might be in search of a tool that makes it easier to manage your storage footprint. In this session, discover ways to extend your storage observability by enabling the new Amazon CloudWatch option in S3 Storage Lens.