Your work for a textile manufacturing company. Your company has hundreds of machines, and each machine has many sensors. Your team used the sensory data to build hundreds of ML models that detect…

Dataflow batch pipelines are not suitable for real-time, sub-millisecond predictions, and while Vertex AI Prediction endpoints offer low latency, coupling them with batch processing won't meet the streaming requirement.

Dataflow batch pipelines do not meet the continuous, sub-millisecond prediction requirement, even with model refresh.

While a Dataflow streaming pipeline and Vertex AI Prediction endpoint with autoscaling can provide real-time predictions, directly integrating models via the RunInference API within Dataflow is often more cost-effective and simpler for large-scale, embedded model use cases with dynamic model updates than managing hundreds of separate Vertex AI endpoints.

A Dataflow streaming pipeline with the RunInference API is ideal for continuous, real-time processing of sensor data and sub-millisecond predictions, handling hundreds of models efficiently. Automatic model refresh ensures models are updated daily without downtime, directly addressing the requirements for 24/7 operation, cost-effectiveness, and responsiveness.