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In-Person
The AI Doubleheader: From Data Strategy to Home Run ResultsTransitioning from an AI pilot to a production-ready environment is the defining challenge for today’s enterprise leaders. It requires more than just a model; it requires a resilient data architecture and a clear path to scalability. Join Pythian and Google Cloud at the Google San Francisco office for a high-impact strategy session where we’ll cover the bases of your AI roadmap. We’ll explore the governance frameworks, infrastructure requirements, and strategic milestones necessary to move beyond experimentation and deliver live, value-driving production environments—followed by an opportunity to watch the San Francisco Giants take on the Arizona Diamondbacks for a 12:45 p.m. game. Consider it a strategy double-header: we’ll tackle the heavy hitting in the morning and catch the home runs in the afternoon. Event Details
Host
Paul BenjaminVice President, Sales |
The AI Doubleheader: From Data Strategy to Home Run Results |
Focusing on AI Production
Hardening the Foundation for Production AI
Scaling from Pilot to Production
Sustaining Production AI Performance
Practical insights for your AI Production roadmap
This session is designed for technology leaders moving past the theoretical and into the actual deployment of AI. The conversation focuses on the specific requirements needed to build a resilient, production-ready environment.
The Game Plan: Giants vs. Diamondbacks
After we conclude our technical briefing at the Google office, the experience moves to the stands for a premier afternoon of baseball and networking. This is followed by an opportunity to watch the San Francisco Giants take on the Arizona Diamondbacks for a 12:45 p.m. game. It’s an exclusive opportunity to step away from the screen and continue the dialogue in a high-energy environment, trading the conference room for the best seats in the house.
Key discussion topics include:
- Overcoming the Pilot-to-Production Gap: Solving the technical hurdles that prevent AI from going live.
- Infrastructure for Production AI: Building the compute and data foundations needed for scaling.
- Governance for Live Environments: Ensuring security and compliance in production-grade AI.
- Measuring Production Results: Establishing the metrics that prove your AI is delivering real ROI.
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