google-cloud-configs

Use when:
- Setting up BigQuery ML for SQL-based machine learning
- Configuring Vertex AI custom training jobs
- Setting up GCP authentication for ML workflows
- Selecting appropriate GPU/TPU configurations
- Estimating costs for GCP ML training
- Deploying models to Vertex AI endpoints
- Configuring distributed training on GCP
- Optimizing cost vs performance for cloud ML

## Platform Overview

### BigQuery ML

**What it is**: SQL-based machine learning directly in BigQuery
**Best for**:
- Quick ML prototypes using existing data warehouse data
- Classification, regression, forecasting on structured data
- Users familiar with SQL but not Python/ML frameworks
- Large-scale batch predictions

**Available Models**:
- Linear/Logistic Regression
- XGBoost (BOOSTED_TREE)
- Deep Neural Networks (DNN)
- AutoML Tables
- TensorFlow/PyTorch imported models

**Pricing**:
- Based on data processed (same as BigQuery queries)
- $5 per TB processed for analysis
- AutoML: $19.32/hour for training

### Vertex AI Training

**What it is**: Fully managed ML training platform
**Best for**:
- Custom PyTorch/TensorFlow training
- Large-scale distributed training
- GPU/TPU-accelerated workloads
- Production ML pipelines

**Available Compute**:
- **CPUs**: n1-standard, n1-highmem, n1-highcpu
- **GPUs**: NVIDIA T4, P4, V100, P100, A100, L4
- **TPUs**: v2, v3, v4, v5e (8 cores to 512 cores)

**Pricing**:
- CPU: $0.05-0.30/hour depending on machine type
- GPU T4: $0.35/hour
- GPU A100: $3.67/hour (40GB) or $4.95/hour (80GB)
- TPU v3: $8.00/hour (8 cores)
- TPU v4: $11.00/hour (8 cores)

## GPU/TPU Selection Guide

### GPU Selection (Vertex AI)

**T4 (16GB VRAM)**:
- Use case: Inference, light training, small models
- Cost: $0.35/hour
- Good for: BERT-base, small CNNs, inference serving

**V100 (16GB VRAM)**:
- Use case: Mid-size training, mixed precision training
- Cost: $2.48/hour
- Good for: ResNet training, medium transformers

**A100 (40GB/80GB VRAM)**:
- Use case: Large model training, dis