Professional Data EngineerOperationalizing machine learning modelsMedium
A financial services company is developing a machine learning model to predict loan default risk. The model will be integrated into their existing loan application system, which requires real-time predictions with low latency. The company needs to ensure that the model is highly available and can scale automatically to handle fluctuating request volumes, especially during peak application periods. Which Google Cloud service is the most appropriate for deploying this model to meet these requirements?
- ABigQuery ML
- BCloud Dataflow
- CCloud Functions
- DVertex AI Prediction
Show answer & explanationAnswer & explanation
Correct answer: D. Vertex AI Prediction
Vertex AI Prediction is designed for deploying ML models for real-time inference with high availability, scalability, and managed infrastructure, making it ideal for low-latency, high-throughput scenarios like loan default prediction.
Why the other options are wrong
- A. BigQuery ML allows training and inference within BigQuery, but it's not optimized for serving real-time, low-latency predictions to external applications.
- B. Cloud Dataflow is primarily for batch and stream data processing, not real-time model serving.
- C. Cloud Functions can serve models but lacks the specialized features for ML model deployment, such as automatic scaling for ML workloads and model versioning, offered by Vertex AI Prediction.
Vertex AI Prediction
A managed service on Google Cloud for deploying machine learning models for online (real-time) or batch predictions.
- Supports various ML frameworks.
- Offers automatic scaling and high availability.
- Provides endpoints for model serving.
Memory trick: Vertex AI is the peak for ML predictions.