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, distributed training
• Cost: $3.67/hour (40GB), $4.95/hour (80GB)
• Good for: GPT-style models, large vision models, multi-GPU training

L4 (24GB VRAM):

• Use case: Modern alternative to T4, better performance
• Cost: $0.66/hour
• Good for: Mid-size models, efficient inference

TPU Selection (Vertex AI)

TPU v2 (8 cores):

• Use case: TensorFlow/JAX training, matrix operations
• Cost: $4.50/hour
• Memory: 8GB per core (64GB total)
• Good for: Legacy TensorFlow models

TPU v3 (8 cores):

• Use case: Standard TPU training
• Cost: $8.00/hour
• Memory: 16GB per core (128GB total)
• Good for: BERT, T5, image classification

TPU v4 (8 cores):

• Use case: Latest generation, best performance
• Cost: $11.00/hour
• Memory: 32GB per core (256GB total)
• Good for: Large language models, cutting-edge research

TPU v5e (8 cores):

• Use case: Cost-optimized TPU
• Cost: $2.50/hour
• Good for: Development, training at scale on budget

Multi-node TPU Pods:

• v3-32: 32 cores, $32/hour
• v3-128: 128 cores, $128/hour
• v4-128: 128 cores, $176/hour
• Use for: Massive distributed training (GPT-3 scale)

Usage

Setup BigQuery ML Environment

bash scripts/setup-bigquery-ml.sh

Prompts for:

• GCP Project ID
• BigQuery dataset name
• Service account credentials
• Default model type preference

Creates:

• bigquery_config.json - Project configuration
• .bigqueryrc - CLI configuration
• Example training SQL in examples/

Setup Vertex AI Training Environment

bash scripts/setup-vertex-ai.sh

Prompts for:

• GCP Project ID
• Region (us-central1, europe-west4, etc.)
• Service account credentials
• Default machine type
• GPU/TPU preference

Creates:

• vertex_config.yaml - Training job configuration
• vertex_requirements.txt - Python dependencies
• Training script template

Configure GCP Authentication

bash scripts/configure-auth.sh

Prompts for:

• Authentication method (service account, user account, workload identity)
• Service account key path (if applicable)
• IAM roles needed

Creates:

• .gcp_auth_config - Authentication configuration
• Sets GOOGLE_APPLICATION_CREDENTIALS environment variable
• Validates permissions

Required IAM Roles:

• BigQuery ML: roles/bigquery.dataEditor, roles/bigquery.jobUser
• Vertex AI: roles/aiplatform.user, roles/storage.objectAdmin
• Both: roles/serviceusage.serviceUsageConsumer

Estimate GCP Training Costs

bash scripts/estimate-gcp-cost.sh

Interactive prompts:

• Platform: BigQuery ML or Vertex AI
• If BigQuery ML: Data size to process
• If Vertex AI:
  • Machine type (CPU/GPU/TPU)
  • Number of machines
  • Training duration estimate
  • Storage requirements

Output:

• Estimated compute cost
• Storage cost
• Data transfer cost (if applicable)
• Total estimated cost
• Cost comparison with other GCP options

Templates

BigQuery ML Training Template (templates/bigquery_ml_training.sql)

SQL template for creating and training models:

• Model creation syntax
• Feature engineering examples
• Training options (L1/L2 reg, learning rate, etc.)
• Evaluation queries
• Prediction queries

Supported model types:

• LINEAR_REG, LOGISTIC_REG
• BOOSTED_TREE_CLASSIFIER, BOOSTED_TREE_REGRESSOR
• DNN_CLASSIFIER, DNN_REGRESSOR
• AUTOML_CLASSIFIER, AUTOML_REGRESSOR

Vertex AI Training Job Template (templates/vertex_training_job.py)

Python template for custom training:

• Training loop structure
• Distributed training setup (PyTorch DDP)
• Checkpointing and model saving
• Metrics logging to Vertex AI
• Hyperparameter tuning integration

Includes:

• Single GPU training
• Multi-GPU training (DataParallel, DistributedDataParallel)
• TPU training with PyTorch/XLA
• Cloud Storage integration

GPU Configuration Template (templates/vertex_gpu_config.yaml)

YAML configuration for GPU training jobs:

• Machine type selection
• GPU type and count
• Disk configuration
• Network configuration
• Environment variables

Presets included:

• Single T4 (budget)
• Single A100 (standard)
• 4x A100 (distributed)
• 8x A100 (large-scale)

TPU Configuration Template (templates/vertex_tpu_config.yaml)

YAML configuration for TPU training jobs:

• TPU type and topology
• TPU version selection
• JAX/TensorFlow runtime
• XLA compilation flags

Presets included:

• v3-8 (single TPU)
• v4-32 (TPU pod slice)
• v5e-8 (cost-optimized)

GCP Authentication Template (templates/gcp_auth.json)

Service account configuration template:

• Project ID
• Service account email
• Key file path
• Required scopes
• IAM role assignments

Security notes:

• Uses placeholders only (never real keys)
• Documents how to create service accounts
• Includes .gitignore protection

Examples

BigQuery ML Regression Example (examples/bigquery-regression-example.sql)

Complete example:

• Dataset: NYC taxi trip data
• Task: Predict trip duration
• Model: BOOSTED_TREE_REGRESSOR
• Includes feature engineering, training, evaluation

Demonstrates:

• CREATE MODEL syntax
• TRANSFORM clause for feature engineering
• MODEL evaluation
• Batch predictions

Vertex AI PyTorch Training Example (examples/vertex-pytorch-training.py)

Complete training script:

• Dataset: IMDB sentiment analysis
• Model: DistilBERT fine-tuning
• Training: Single GPU
• Logging: Vertex AI experiments

Demonstrates:

• Loading data from GCS
• Training loop with mixed precision
• Checkpointing to GCS
• Metrics logging
• Model export to Vertex AI

Vertex AI Distributed Training Example (examples/vertex-distributed-training.py)

Multi-GPU training example:

• Dataset: ImageNet subset
• Model: ResNet-50
• Training: 4x A100 with DDP
• Scaling: Linear scaling rule

Demonstrates:

• PyTorch DistributedDataParallel
• Gradient accumulation
• Learning rate scaling
• Synchronized batch norm
• Multi-node coordination

Hugging Face Fine-tuning on Vertex AI (examples/vertex-huggingface-finetuning.py)

Production fine-tuning template:

• Dataset: Custom text classification
• Model: BERT/RoBERTa/DeBERTa
• Training: Hugging Face Trainer API
• Deployment: Vertex AI endpoint

Demonstrates:

• Hugging Face Trainer integration
• Hyperparameter tuning with Vertex AI
• Model versioning
• Endpoint deployment
• Online predictions

Cost Optimization Tips

BigQuery ML

Reduce data processed:

• Use partitioned tables
• Filter data in WHERE clause before training
• Use table sampling for experimentation
• Cache intermediate results

Use appropriate model types:

• Start with LINEAR_REG/LOGISTIC_REG (cheapest)
• Use BOOSTED_TREE for better accuracy at moderate cost
• Reserve AutoML for when simpler models fail

Optimize queries:

• Avoid SELECT * (specify columns)
• Use clustering on filter columns
• Materialize views for repeated training

Vertex AI

Machine type selection:

• Start with CPU for prototyping
• Use T4 for small models (cheapest GPU)
• Use A100 only for large models that need it
• Consider TPU v5e for TensorFlow/JAX (very cost-effective)

Training optimization:

• Use preemptible instances (60-70% cheaper, can be interrupted)
• Enable automatic checkpoint/resume for preemptible
• Use mixed precision training (FP16/BF16) for faster training
• Profile to eliminate CPU bottlenecks

Storage optimization:

• Store datasets in Cloud Storage (cheaper than persistent disk)
• Use Filestore only if needed for POSIX filesystem
• Clean up old model artifacts
• Use lifecycle policies to archive old data

Multi-GPU efficiency:

• Ensure near-linear scaling before adding more GPUs
• Profile inter-GPU communication
• Use gradient accumulation instead of larger batch sizes
• Consider 2x GPUs instead of 1x larger GPU (often same cost, better availability)

Integration with ML Training Plugin

This skill integrates with other ml-training components:

• training-patterns: Provides GCP configs for generated training scripts
• cost-calculator: Uses GCP pricing data for budget planning
• monitoring-dashboard: Integrates with Vertex AI TensorBoard
• validation-scripts: Validates GCP credentials and permissions
• integration-helpers: Deploys trained models to Vertex AI endpoints

Common Workflows

Workflow 1: Quick BigQuery ML Prototype

1. Run bash scripts/setup-bigquery-ml.sh
2. Copy templates/bigquery_ml_training.sql to your project
3. Modify SQL for your dataset and features
4. Run training query in BigQuery console
5. Evaluate with built-in ML.EVALUATE()
6. Export predictions with ML.PREDICT()

Time: 30 minutes setup + training time Cost: $5 per TB of data processed

Workflow 2: Custom PyTorch Training on Vertex AI

1. Run bash scripts/configure-auth.sh
2. Run bash scripts/setup-vertex-ai.sh
3. Copy templates/vertex_training_job.py
4. Customize training loop for your model
5. Copy templates/vertex_gpu_config.yaml
6. Submit job: gcloud ai custom-jobs create ...
7. Monitor in Vertex AI console

Time: 1 hour setup + training time Cost: Depends on GPU/TPU selection

Workflow 3: Large-Scale Distributed Training

1. Setup Vertex AI (workflow 2)
2. Copy examples/vertex-distributed-training.py
3. Adapt for your model architecture
4. Test locally with 1 GPU
5. Test with 2 GPUs to verify scaling
6. Scale to 4-8 GPUs for full training
7. Use preemptible instances with checkpointing

Time: 2-4 hours setup + training time Cost: $15-60/hour depending on GPU count

Troubleshooting

BigQuery ML Issues

"Insufficient permissions":

• Verify roles/bigquery.dataEditor and roles/bigquery.jobUser
• Check dataset-level permissions
• Ensure billing is enabled

"Model training failed":

• Check for NULL values in features
• Verify data types match model expectations
• Review feature engineering TRANSFORM clause
• Check for sufficient training data

Vertex AI Issues

"Service account lacks permissions":

• Verify roles/aiplatform.user
• Add roles/storage.objectAdmin for GCS access
• Check project-level IAM policies

"GPU/TPU quota exceeded":

• Request quota increase in GCP console
• Use different region with availability
• Start with smaller GPU/TPU configuration
• Use preemptible instances (separate quota)

"Training job crashes":

• Check for CUDA OOM (reduce batch size)
• Verify dependencies in requirements.txt
• Review logs in Cloud Logging
• Test locally before submitting to Vertex

Security Best Practices

Credentials Management

DO:

• ✅ Use service accounts with minimal permissions
• ✅ Store credentials in Secret Manager
• ✅ Use Workload Identity for GKE deployments
• ✅ Rotate service account keys regularly
• ✅ Add .gitignore for *.json key files

DON'T:

• ❌ Hardcode credentials in code
• ❌ Commit service account keys to git
• ❌ Use overly permissive roles (e.g., Owner)
• ❌ Share service account keys across projects
• ❌ Use personal credentials for production

IAM Best Practices

• Use separate service accounts for training vs serving
• Grant roles at resource level, not project level when possible
• Use Workload Identity Federation instead of keys when possible
• Enable Cloud Audit Logs for ML API usage
• Review IAM permissions quarterly

Performance Benchmarks

BigQuery ML vs Vertex AI

BigQuery ML:

• Best for: Structured data, SQL users, quick prototypes
• Training time: Minutes to hours (depends on data size)
• Scalability: Automatic (serverless)
• Cost: $5/TB processed

Vertex AI Custom Training:

• Best for: Deep learning, custom architectures, GPU/TPU workloads
• Training time: Hours to days (configurable hardware)
• Scalability: Manual (choose machine type)
• Cost: $0.35-20/hour depending on hardware

Rule of thumb:

• Use BigQuery ML for tabular data with < 100M rows
• Use Vertex AI for images, text, audio, or custom models
• Use Vertex AI for models requiring GPU/TPU acceleration

Additional Resources

• GCP ML Documentation: https://cloud.google.com/vertex-ai/docs
• BigQuery ML Reference: https://cloud.google.com/bigquery-ml/docs
• Pricing Calculator: https://cloud.google.com/products/calculator
• TPU Best Practices: https://cloud.google.com/tpu/docs/best-practices
• Vertex AI Samples: https://github.com/GoogleCloudPlatform/vertex-ai-samples