|
Physical AI is moving from pilot to production. In 2027, more organizations will rely on models that sense conditions across factories, energy systems and distributed operations, then act and adapt. Those models depend on a continuous, high-resolution record of what the equipment actually did.
At the machine, that record has to support decisions made in milliseconds on modest hardware. At the hub, it has to feed training and analysis across every site. Many teams start with a system built for one of those jobs and extend it to cover the other, and that's often where data gets downsampled, delayed or lost.
Join InfluxData to learn:
- Why physical AI needs a different data foundation than generative AI
- How an edge-to-cloud time series architecture supports real-time inference at the machine and training at the hub
- How to retain full-fidelity telemetry with predictable costs
- What to prioritize now for 2027, without replacing existing systems
Register Now to attend the webinar Physical AI in 2027: Real-Time Data, Edge Architecture and Fidelity.
Don't miss this live event on
Tuesday, November 10, 11:00 AM PT / 2:00 PM ET.
|