Hands-on labs to turn your data into AI-powered action

The shift from static dashboards to dynamic, AI-empowered systems marks a pivotal moment for data teams. Organizations no longer ask what happened—they demand platforms that act on insights in real time. Yet, transforming raw data into autonomous workflows requires more than theory; it demands hands-on experience with cutting-edge tools.

To bridge this gap, Google Cloud is rolling out its Data Cloud Labs: Agentic AI series in Toronto and Chicago this June. These in-person workshops aren’t about passive lectures. Instead, they immerse participants in live, engineer-led sessions where teams build and deploy AI-ready data architectures from scratch.

Designed for practitioners, not spectators

This isn’t a conference where attendees nod along to slides. It’s a full-day of collaborative problem-solving, tailored for data professionals who want to move beyond dashboards. Whether you’re a data engineer tuning pipelines, a scientist refining AI models, or an analyst exploring multimodal insights, the labs focus on practical, immediately applicable skills.

Participants will work directly with Google Cloud’s latest offerings, guided by engineers who’ve implemented these solutions in production. The goal? To equip teams with the architectural patterns and technical know-how needed to make their data stacks not just analytical, but actionable.

What you’ll build—and take home

Bring your laptop and a willingness to get technical. Over the course of the day, you’ll construct a complete agentic workflow, step by step. Here’s a preview of the core modules:

• Governed data ingestion at scale
• Design unified pipelines that pull data from multi-cloud sources into a single, governed layer.
• Use Apache Spark and Google Cloud’s Knowledge Catalog to enforce data quality and lineage, ensuring compliance and consistency across environments.

• Multimodal analytics with Gemini in BigQuery
• Move beyond traditional tabular data. Extract structured insights from images, audio, and text using Google’s multimodal AI capabilities.
• Learn to query and analyze unstructured datasets directly in BigQuery, reducing the need for costly preprocessing.

• High-performance vector search in AlloyDB
• Implement vectorized search to power context-aware AI applications, such as recommendation engines or semantic search tools.
• Scale these systems efficiently to handle millions of embeddings without sacrificing latency.

• Autonomous agentic workflows
• Assemble the pieces into a working agent that doesn’t just analyze data—but acts on it.
• Leverage BigQuery Graph and the Agent Development Kit (ADK) to create workflows that trigger actions, such as updating dashboards, sending alerts, or even initiating downstream processes like inventory restocking.

By the end of the lab, you won’t just understand these concepts—you’ll have a functional prototype ready to adapt for your organization’s needs.

Why these labs matter now

The demand for agentic AI isn’t theoretical. Companies like Airbnb and Shopify have already deployed systems that autonomously adjust pricing, personalize recommendations, and even manage customer support tickets—all driven by live data. The barrier to entry isn’t just access to tools; it’s knowing how to integrate them into end-to-end, production-grade workflows.

These labs remove that barrier. They provide a low-risk environment to experiment, fail, and refine under expert guidance, ensuring that when you return to your team, you’re not just reporting on trends—you’re driving them.

How to secure your spot

Space is strictly limited to ensure personalized support and hands-on assistance. Registration is open now for two dates and locations:

• Toronto: June 25 at Delta Hotels Toronto
• Chicago: June 30 at Google Chicago Office

If you’re serious about turning your data from a reporting asset into a strategic engine, these labs offer the fastest path to mastery. Register today and join the teams already building the future of applied AI.