John Mallory

AI is multiplying the identities and agents that can change your AWS data. One over-scoped role or runaway agent can delete or overwrite the data your models depend on, in an S3 lake or Iceberg lakehouse.

Not all latency-sensitive workloads have the same storage requirements. From AI/ML training checkpoints to real-time analytics and high-frequency data pipelines, choosing between Amazon S3 Express One Zone, Amazon FSx for Lustre, and instance storage depends on access patterns, throughput needs, and cost constraints.

Learn how Amazon S3 and S3 Vectors turn data lakes into intelligent, searchable foundations for enterprise AI. See how Spice AI uses its open-source data and AI platform on AWS to integrate S3 Vectors with open table formats, enabling hybrid SQL and vector search directly on S3 without complex data movement.

Scaling deep learning programs into production often comes with its challenges, and you can’t have high-quality data modeling if your storage isn’t optimized for performance scalability. Getting this wrong can lead to unexpected cost and performance issues.

Scaling deep learning programs into production often comes with its challenges, and you can’t have high-quality data modeling if your storage isn’t optimized for performance scalability. Getting this wrong can lead to unexpected cost and performance issues.

Scaling deep learning programs into production often comes with its challenges, and you can’t have high quality data modeling if your storage isn’t optimized for performance scalability. Getting this wrong can lead to unexpected cost and performance issues.

Flexibility is key when building and scaling a data lake, and by choosing the right storage architecture you will have agility to quickly experiment and migrate with the latest analytics solutions. In this session, we explore the best practices for building a data lake on Amazon S3 that allows you to leverage an entire array of AWS, open-source, and third-party analytics tools, helping you...

Flexibility is key when building and scaling a data lake, and by choosing the right storage architecture you will have agility to quickly experiment and migrate with the latest analytics solutions. In this session, we explore the best practices for building a data lake on Amazon S3 that allows you to leverage an entire array of AWS, open-source, and third-party analytics tools, helping you...

AWS offers tools and services that make analyzing and processing petabytes of data in the cloud faster, simpler, and more cost effective. In this chalk talk, AWS experts provide an overview of our services for querying data in place, such as Amazon S3 Select, Glacier Select, Amazon Athena, and Amazon Redshift Spectrum.