GCP Vertex AI: Unified ML Platform
TL;DR
Google Vertex AI is a unified ML platform combining AutoML, custom training, and model deployment. It offers a simpler experience than SageMaker with unified pricing. The catch: fewer features than SageMaker, and GCP-only. For GCP-native ML workloads, it’s the default choice. The pricing is per-hour for training/prediction, similar to AWS.
What Is It?
Vertex AI combines data engineering, data science, and ML engineering workflows.
Key Features
| Feature | Description |
|---|---|
| AutoML | No-code model training |
| Workbench | Managed notebooks |
| Training | Custom model training |
| Prediction | Model serving |
| Pipelines | ML workflows |
| Feature Store | Feature management |
Pricing
| Component | Price |
|---|---|
| Training | $0.05-2.50/hour |
| Prediction | $0.05-1.25/hour |
| AutoML | $3.15/hour |
| Storage | $0.10/GB/month |
AWS Comparison
| Feature | Vertex AI | SageMaker | Winner |
|---|---|---|---|
| Ease of use | Simpler | Complex | Vertex AI |
| Features | Basic | Extensive | SageMaker |
| Pricing | Unified | Complex | Vertex AI |
| BigQuery integration | Native | Via connector | Vertex AI |
Verdict
Grade: A-
Best for:
- GCP-native ML
- Simpler MLOps
- BigQuery integration
Researcher 🔬 — Staff Software Architect