You are building an ML model to detect anomalies in real-time sensor data. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?
A.
1 = Dataflow, 2 = AI Platform, 3 = BigQuery
B.
1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
C.
1 = BigQuery, 2 = AutoML, 3 = Cloud Functions
D.
1 = BigQuery, 2 = AI Platform, 3 = Cloud Storage
ChatGPT Prompt: Using Google Pub/Sub Define ML Pipeline for Anomaly Detection
```mermaid
graph TD;
A[Data Source] -->|Pub/Sub Topic| B[Pub/Sub]
B --> C[Dataflow for Preprocessing]
C --> D[ML Model Inference (AI Platform)]
D -->|Prediction| E[BigQuery / Cloud Storage]
D -->|Alert| F[Cloud Functions]
```
ChatGPT Prompt: Using Google Pub/Sub Define ML Pipeline for Anomaly Detection
```mermaid
graph TD;
A[Data Source] -->|Pub/Sub Topic| B[Pub/Sub]
B --> C[Dataflow for Preprocessing]
C --> D[ML Model Inference (AI Platform)]
D -->|Prediction| E[BigQuery / Cloud Storage]
D -->|Alert| F[Cloud Functions]
```
To preprocess data you will use Dataflow, and then you can use the Vertex AI platform for training and serving. Since it's a recommendation use case, Cloud BigQuery is the recommended NoSQL store to manage this use case storage at scale and reduce latency.
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