Oren Waldman

Every agentic system works on the happy path. The real test is what happens when your AI agent times out mid-reasoning, returns a malformed action, or cascades a single failure across an entire chain.

Enterprise data teams maintain ETL pipelines built on legacy tools like stored procedures, custom bash scripts, IBM DataStage and others, that resist migration due to undocumented dependencies.

Data lakes require comprehensive security controls throughout their lifecycle, particularly for AI/ML workloads. This session provides hands-on experience implementing end-to-end security for AWS data lake architectures.

The cloud promises lower costs, greater innovation, higher resiliency, and other benefits for customers who make the investment to adopt cloud. But how can a customer measure the ROI of their cloud investment?

Due to lack of time or resources, DevSecOps engineers are not always able to triage quickly enough. What if you had a chatbot customized for your environment’s use case?

In today’s data-driven world, businesses are constantly seeking ways to harness the power of data to gain insights, make informed decisions, and stay ahead of the competition. However, setting up a data lake traditionally requires technical expertise and extensive coding knowledge.