Your company wants to develop a machine learning model that predicts equipment failures based on historical and real-time data. What combination of GCP services would be most appropriate for gathering data, training the model, and serving predictions?

Pub/Sub is ideal for ingesting real-time data streams, such as sensor data from equipment, which is crucial for predicting failures based on current conditions. AI Platform (now Vertex AI) provides a comprehensive suite of tools for training, deploying, and managing machine learning models, making it suitable for both model development and serving predictions. BigQuery and Dataflow are excellent for large-scale data warehousing and batch processing, but less optimal for real-time ingestion and direct model serving. IoT Core is for device management, and AutoML is for automated ML, but they don't cover the full pipeline of real-time ingestion, custom model training, and serving as effectively as Pub/Sub and AI Platform combined. Firestore and Cloud Functions are good for application backends, not primary ML pipelines. Dataprep is for data preparation, and Kubernetes Engine is for container orchestration, neither of which directly addresses the full ML lifecycle for this use case.