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.
This use case similar to anomaly detection also points to A only.
https://cloud.google.com/blog/products/data-analytics/anomaly-detection-using-streaming-analytics-and-ai
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