Lilly Tech Systems
Introduction to Vertex AI Beginner
Vertex AI is Google Cloud's unified machine learning platform that brings together all GCP ML services under a single API and UI. It covers the entire ML lifecycle from data preparation and model training to deployment and monitoring.
Vertex AI Components
| Component | Purpose |
|---|---|
| Workbench | Managed JupyterLab notebooks for experimentation and development |
| Training | Custom and AutoML training jobs on managed compute |
| Pipelines | Orchestrate ML workflows with Kubeflow Pipelines |
| Model Registry | Version, track, and manage trained models |
| Endpoints | Deploy models for online and batch predictions |
| Feature Store | Manage and serve ML features at scale |
| Model Monitoring | Detect data drift and prediction anomalies |
| Experiments | Track and compare training runs |
Enabling Vertex AI APIs
Bash
# Enable required APIs
gcloud services enable aiplatform.googleapis.com
gcloud services enable notebooks.googleapis.com
gcloud services enable compute.googleapis.com
gcloud services enable storage.googleapis.com
gcloud services enable containerregistry.googleapis.com
The Vertex AI Workflow
- Develop
Use Workbench notebooks to explore data and prototype models.
- Train
Submit custom training jobs or use AutoML for managed training.
- Evaluate
Compare model performance using Vertex AI Experiments.
- Deploy
Create Endpoints and deploy models for serving predictions.
- Monitor
Set up model monitoring for drift detection and alerts.
- Automate
Build Pipelines to orchestrate the entire workflow end-to-end.
Key Insight: Vertex AI abstracts away infrastructure management. You focus on data and models while Google manages compute clusters, scaling, and security. This makes it ideal for teams that want to move quickly without building custom ML infrastructure.