Gcp Ml Infrastructure Manager

---
name: gcp-ml-infrastructure-manager
description: Google Cloud Platform infrastructure management for BigQuery ML and Vertex AI production deployments with authentication, GPU/TPU provisioning, distributed training orchestration, and enterprise cost controls. Use when deploying production ML infrastructure on GCP, configuring multi-region Vertex AI training, setting up BigQuery ML pipelines, provisioning TPU pods, implementing cost governance, or when user mentions GCP ML production, enterprise training infrastructure, cloud ML at scale, or Google Cloud deployment automation.
allowed-tools: Bash, Read, Write, Edit
---

Use when:
- Deploying production BigQuery ML infrastructure
- Orchestrating large-scale Vertex AI training jobs
- Managing GCP authentication for ML pipelines
- Provisioning multi-GPU and TPU pod configurations
- Implementing enterprise cost controls for cloud ML
- Automating model deployment to Vertex AI endpoints
- Configuring distributed training across GCP regions
- Optimizing production ML infrastructure performance

## Platform Overview

### BigQuery ML

**What it is**: SQL-based machine learning directly in BigQuery
**Best for**:
- Production ML on data warehouse data
- Classification, regression, forecasting on structured data
- Teams using SQL for ML workflows
- Large-scale batch prediction pipelines

**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 for production workloads
**Best for**:
- Custom PyTorch/TensorFlow production training
- Large-scale distributed training infrastructure
- GPU/TPU-accelerated production workloads
- Enterprise ML deployment 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 serving, light training, development
- Cost: $0.35/hour
- Good for: BERT-base, small CNNs, production inference

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

**A100 (40GB/80GB VRAM)**:
- Use case: Large model training, distributed production 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 production alternative to T4
- Cost: $0.66/hour
- Good for: Mid-size models, efficient production inference

### TPU Selection (Vertex AI)

**TPU v2 (8 cores)**:
- Use case: TensorFlow/JAX training
- Cost: $4.50/hour
- Memory: 8GB per core (64GB total)

**TPU v3 (8 cores)**:
- Use case: Standard production 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 production workloads
- Cost: $11.00/hour
- Memory: 32GB per core (256GB total)
- Good for: Large language models, research at scale

**TPU v5e (8 cores)**:
- Use case: Cost-optimized production 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 infrastructure

## Usage

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### Setup BigQuery ML Production Environment

```bash
bash scripts/setup-bigquery-ml-production.sh
```

**Configuration prompts**:
- GCP Project ID and billing account
- BigQuery dataset name and region
- Service account credentials with appropriate IAM roles
- Production model type preferences
- Data residency and compliance requirements

**Creates**:
- `bigquery_production_config.json` - Production configuration
- `.bigqueryrc` - CLI configuration
- Production training SQL templates in examples/
- Monitoring and alerting configurations

### Setup Vertex AI Production Training Environment

```bash
bash scripts/setup-vertex-ai-production.sh
```

**Configuration prompts**:
- GCP Project ID and organization
- Region selection (us-central1, europe-west4, asia-southeast1)
- Service account credentials
- Default machine type for production
- GPU/TPU provisioning preferences
- Multi-region deployment settings

**Creates**:
- `vertex_production_config.yaml` - Production training job configuration
- `vertex_requirements.txt` - Python dependencies
- Production training script templates
- Infrastructure-as-code configurations

### Configure GCP Production Authentication

```bash
bash scripts/configure-production-auth.sh
```

**Authentication setup**:
- Authentication method (service account, workload identity, ADC)
- Service account key management
- IAM role validation and assignment
- Multi-project credential management

**Creates**:
- `.gcp_auth_config` - Authentication configuration
- Sets GOOGLE_APPLICATION_CREDENTIALS
- Validates production permissions
- Configures credential rotation

**Required Production IAM Roles**:
- BigQuery ML: `roles/bigquery.dataEditor`, `roles/bigquery.jobUser`
- Vertex AI: `roles/aiplatform.user`, `roles/storage.objectAdmin`
- Both: `roles/serviceusage.serviceUsageConsumer`
- Monitoring: `roles/monitoring.metricWriter`

### Estimate GCP Production Costs

```bash
bash scripts/estimate-gcp-production-cost.sh
```

**Cost analysis for**:
- Platform: BigQuery ML or Vertex AI production deployments
- If BigQuery ML: Production data volume
- If Vertex AI:
  - Machine type (CPU/GPU/TPU)
  - Number of machines and scaling
  - Estimated training duration
  - Storage and networking requirements
  - Multi-region considerations

**Output**:
- Estimated compute cost with production SLA
- Storage cost projections
- Data transfer and egress costs
- Total estimated production cost
- ROI comparison with other cloud providers

## Templates

### BigQuery ML Production Template (`templates/bigquery_ml_production.sql`)

SQL template for production model training:
- Production model creation syntax
- Feature engineering pipelines
- Training options optimized for production
- Production evaluation queries
- Batch prediction at scale

**Supported production model types**:
- LINEAR_REG, LOGISTIC_REG
- BOOSTED_TREE_CLASSIFIER, BOOSTED_TREE_REGRESSOR
- DNN_CLASSIFIER, DNN_REGRESSOR
- AUTOML_CLASSIFIER, AUTOML_REGRESSOR

### Vertex AI Production Training Template (`templates/vertex_production_job.py`)

Python template for production custom training:
- Production training loop structure
- Distributed training setup (PyTorch DDP, Horovod)
- Production checkpointing and model versioning
- Metrics logging to Vertex AI and Cloud Monitoring
- Hyperparameter tuning for production

**Includes**:
- Single GPU production training
- Multi-GPU production training
- TPU production training with PyTorch/XLA
- Cloud Storage integration with versioning
- Automated deployment pipelines

### GPU Production Configuration (`templates/vertex_gpu_production_config.yaml`)

YAML configuration for production GPU training:
- Machine type selection for production SLA
- GPU type, count, and availability zones
- Persistent disk configuration
- Network configuration for multi-region
- Environment variables and secrets management

**Production presets**:
- Single T4 (production inference)
- Single A100 (production training)
- 4x A100 (distributed production)
- 8x A100 (large-scale production)

### TPU Production Configuration (`templates/vertex_tpu_production_config.yaml`)

YAML configuration for production TPU training:
- TPU type and topology for production
- TPU version selection and availability
- JAX/TensorFlow runtime optimization
- XLA compilation flags for performance

**Production presets**:
- v3-8 (single TPU production)
- v4-32 (TPU pod production)
- v5e-8 (cost-optimized production)

## Production Workflows

### Workflow 1: Production BigQuery ML Pipeline

1. Run `bash scripts/setup-bigquery-ml-production.sh`
2. Configure production data access and permissions
3. Copy `templates/bigquery_ml_production.sql`
4. Customize for production dataset and features
5. Deploy with automated retraining schedule
6. Monitor with Cloud Monitoring dashboards
7. Implement production prediction serving

**Setup time**: 2-4 hours
**Production cost**: $5 per TB processed + serving costs

### Workflow 2: Production PyTorch Training on Vertex AI

1. Run `bash scripts/configure-production-auth.sh`
2. Run `bash scripts/setup-vertex-ai-production.sh`
3. Copy `templates/vertex_production_job.py`
4. Implement production training loop
5. Configure `templates/vertex_gpu_production_config.yaml`
6. Deploy with CI/CD integration
7. Monitor production metrics in real-time

**Setup time**: 4-8 hours
**Production cost**: Based on GPU/TPU selection

### Workflow 3: Large-Scale Production Distributed Training

1. Setup Vertex AI production environment
2. Copy production distributed training templates
3. Implement model architecture for production
4. Validate with small-scale production test
5. Scale to production multi-GPU/TPU configuration
6. Deploy with automated failover and checkpointing
7. Implement production monitoring and alerting

**Setup time**: 8-16 hours
**Production cost**: $15-60/hour based on infrastructure

## Production Security Best Practices

### Credentials Management for Production

**Production requirements**:
- ✅ Use service accounts with least-privilege permissions
- ✅ Store credentials in Secret Manager with rotation
- ✅ Use Workload Identity for production GKE
- ✅ Implement automated key rotation
- ✅ Configure VPC Service Controls

**Production restrictions**:
- ❌ Never hardcode credentials in production code
- ❌ Never commit service account keys to repositories
- ❌ Avoid overly permissive production roles
- ❌ No shared credentials across environments
- ❌ No personal credentials in production

### Production IAM Architecture

- Separate service accounts for training vs serving
- Resource-level permissions for production isolation
- Workload Identity Federation for production
- Cloud Audit Logs for all production API usage
- Quarterly production security reviews

## Production Performance Benchmarks

### BigQuery ML Production Characteristics

**Production use cases**:
- Best for: Structured data, production SQL pipelines
- Training time: Minutes to hours (automatic scaling)
- Scalability: Serverless production infrastructure
- Production cost: $5/TB processed

**Production SLA**: 99.9% uptime guarantee

### Vertex AI Production Characteristics

**Production use cases**:
- Best for: Deep learning, production GPU/TPU workloads
- Training time: Hours to days (configurable infrastructure)
- Scalability: Manual provisioning for production
- Production cost: $0.35-20/hour based on configuration

**Production SLA**: 99.9% uptime with multi-region

## Additional Production Resources

- **GCP ML Production Documentation**: https://cloud.google.com/vertex-ai/docs/production
- **BigQuery ML Production Guide**: https://cloud.google.com/bigquery-ml/docs/production
- **Production Pricing Calculator**: https://cloud.google.com/products/calculator
- **TPU Production Best Practices**: https://cloud.google.com/tpu/docs/production
- **Vertex AI Production Samples**: https://github.com/GoogleCloudPlatform/vertex-ai-production