Google Cloud Labs: AI Toolkit
Register here: https://rsvp.withgoogle.com/events/google-cloud-labs-ai-toolkit-toronto2 Join Google Cloud Labs for an immersive, hands-on workshop designed to provide you with the practical skills and architectural patterns needed to deploy secure and scalable AI on GKE and integrate applied AI solutions across Google’s Data Cloud. Please fill out the registration form below to express interest in joining the event! Space is limited, and because we want to make sure everyone gets a chance to learn, priority will be granted to folks who weren't able to catch us at the June session. Registration is required, so secure your spot on the waitlist today! Welcome to The Cloud Engineer's AI Toolkit workshop! This hands-on workshop will provide you with the practical skills and architectural patterns needed to deploy secure and scalable AI on GKE and integrate applied AI solutions across Google’s Data Cloud. This is an interactive session, not a lecture. Get ready for a series of live labs where you will use GKE OneMCP and the Gemini CLI to accelerate operations, and implement the GKE Agent Sandbox for secure code execution. In our data lab, we’ll guide you through building multimodal pipelines to power BigQuery Graph analytics and agentic workflows in ADK. You will leave with the practical expertise to deploy, secure, and scale production-grade AI workloads. Note: To participate, you must bring your own laptop and power cable. The activities require a laptop and cannot be completed on a tablet or phone. Who Should Attend? This program is designed for Platform and Security Engineers, as well as Data Practitioners. Whether your focus is implementing defense-in-depth strategies, such as securing inference endpoints and enforcing security policies via Workload Identity and Policy Controller, or building multimodal data platforms that power agentic workflows, this session is for you. To get the most out of the labs, you should have foundational knowledge of GKE, and containerization, and basic SQL, alongside a commitment to maintaining auditable, scalable security for AI workloads.
