You must increase operating efficiency across 20 million cellular and disconnected vehicles by adjusting operational parameters automatically. How should you proceed?

The correct solution addresses the scale (20 million vehicles) and the disconnected nature of the vehicles. Training ML models to identify ideal operations is scalable and effective for complex parameter adjustments. Running models locally on the vehicle allows for real-time adjustments even when disconnected, which is crucial for cellular and disconnected environments. Inspecting data manually for 20 million vehicles is not scalable. A rules-based algorithm might be too rigid and difficult to maintain for complex, dynamic operational parameters. Cloud Dataflow streaming with Google Cloud Messaging would require constant connectivity, which is not guaranteed for "disconnected vehicles." Hosting models on Cloud ML Platform for real-time adjustments would also require continuous connectivity to the cloud, making it unsuitable for disconnected vehicles. Local execution is key here.