GCP Vertex AI

⬢ NÍVEL 2Técnicas

Alto

Impacto salarial

3 meses

Tempo de aprendizado

Difícil

Dificuldade

—

Carreiras

Em resumo

Vertex AI is Google's end-to-end ML platform. Train models (AutoML, custom), deploy as endpoints, monitor performance. Handles data pipelines, model training, serving, monitoring. Mastery takes 2-3 months. Senior practitioners earn 20-30% premium. Adjacent skills: TensorFlow, Python, statistics.

O que é GCP Vertex AI

Vertex AI is Google's unified machine learning platform. Train models (tabular, images, text), deploy as scalable endpoints, monitor performance. Integrate with BigQuery (data source), Cloud Storage (data), and other GCP services. Handles the full ML lifecycle: data preparation → training → evaluation → deployment → monitoring. ML is becoming essential. Companies need models to compete (personalization, prediction, automation). Vertex AI abstracts complexity: no need to manage Kubernetes clusters for training, model serving is handled. Senior practitioners earn 20-30% premium because ML is scarce and high-value.

🔧 FERRAMENTAS E ECOSSISTEMA

Google Cloud ConsoleVertex AI SDK (Python)AutoML for low-code MLCustom training (bring your own code)Model serving and endpointsBigQuery ML integrationTensorFlow / PyTorchVertex AI Pipelines (MLOps)

📋 Antes de começar

Python

💰 Salário por região

Região	Júnior	Pleno	Sênior
USA	$90k	$155k	$240k
UK	$65k	$120k	$185k
EU	$70k	$130k	$200k
CANADA	$95k	$160k	$250k

🎓 Certificações

Google Cloud Professional Machine Learning Engineer Vertex AI Documentation

⚖ Comparar com

Hugging Face Transformers

❓ Perguntas frequentes

What's the difference between AutoML and custom training?

AutoML: low-code, Google handles hyperparameter tuning, fast iteration. Custom training: you control everything, flexible but more work. Use AutoML for baseline, custom training for production optimizations.

How do I deploy a model to production?

Create an Endpoint (managed service). Upload your model. Vertex AI handles scaling, load balancing, versioning. Send prediction requests to the endpoint. Auto-scales based on load.

Can I retrain models automatically?

Yes. Vertex AI Pipelines orchestrates retraining workflows (fetch fresh data, train, evaluate, deploy). Scheduled retraining (daily, weekly) keeps models fresh.

How do I monitor model performance in production?

Vertex AI Model Monitoring detects data drift (input distribution changed) and prediction drift (output distribution changed). Alerts when model quality degrades.

What's BigQuery ML?

SQL interface for ML. Write a SELECT query, BigQuery trains a model. No Python required. Good for analysts, SQL experts. Limited to simpler models.

How much does Vertex AI cost?

Training: per-VM-hour. Serving: per-node-hour. Example: train a model on 4 GPUs for 10 hours = 40 GPU-hours = ~$400 (varies by region). Serving endpoint: 1 node = ~$100/mo.

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