Get ready to ingest data and transform it into ready-to-use datasets using Python. We'll share a no-nonsense approach for developing and testing data connectors and transformations locally. Moving to production will be a matter of tweaking your configuration. In the end, you get a simple dataset interface to build dashboards & applications, train predictive models, or create agentic workflows. This workshop includes two guest speakers. Brian teach how to leverage AI IDEs, MCP servers and LLM scaffoldings to create ingestion pipelines. Elvis will show how to interactively define transformations and data quality checks.

Everyone's trying to make LLMs "accurate." But the real challenge isn't accuracy — it's context. We'll explore why traditional approaches like evals suites or synthetic question sets fall short, and how successful AI systems are built instead through compounding context over time. Hex enables a new workflow for conversational analytics that grows smarter with every interaction. With Hex's Notebook Agent and Threads, business users define the questions that matter while data teams refine, audit, and operationalize them into durable, trusted workflows. In this model, "tests" aren't written in isolation by data teams — they're defined by the business and operationalized through data workflows. The result is a living system of context — not a static set of prompts or tests — that evolves alongside your organization. Join us for a candid discussion on what's working in production AI systems, and get hands-on building context-aware analytical workflows in Hex!