AI/ML Pipeline Architecture¶

A comprehensive guide to building end-to-end machine learning pipelines with MLOps practices across AWS, Azure, and Google Cloud Platform.

Table of Contents¶

1. Architecture Overview
2. Architecture Diagram Description
3. Component Breakdown
4. AWS Implementation
5. Azure Implementation
6. GCP Implementation
7. MLOps Practices
8. Feature Stores
9. Model Monitoring
10. Cost Estimation
11. Production Checklist

Architecture Overview¶

What is an ML Pipeline?¶

An ML pipeline is an automated workflow that covers the full machine learning lifecycle: - Data Preparation: Collecting, cleaning, and transforming training data - Feature Engineering: Creating and managing features for model training - Model Training: Training models on prepared data with hyperparameter tuning - Model Evaluation: Validating model performance against metrics and baselines - Model Deployment: Serving models for real-time or batch inference - Model Monitoring: Tracking model performance and detecting drift in production

ML Pipeline Stages¶

1. Data Ingestion: Collect raw data from various sources
2. Data Validation: Check data quality, schema, and distribution
3. Data Preprocessing: Clean, normalize, and transform data
4. Feature Engineering: Create and select features
5. Model Training: Train models with experiment tracking
6. Model Evaluation: Compare against metrics and baselines
7. Model Registration: Version and store approved models
8. Model Deployment: Deploy to serving infrastructure
9. Monitoring: Track predictions, performance, and data drift
10. Retraining: Trigger retraining when performance degrades

Benefits¶

• Reproducibility: Versioned data, code, and models enable reproducible experiments
• Automation: Reduce manual steps and accelerate iteration
• Quality: Automated validation catches issues before production
• Governance: Audit trail for model decisions and compliance
• Scalability: Handle large datasets and multiple models efficiently
• Collaboration: Teams share features, experiments, and models

Trade-offs¶

• Complexity: ML pipelines have many moving parts
• Cost: GPU/TPU training, feature stores, and serving infrastructure add cost
• Skill Requirements: Requires ML engineering and platform expertise
• Infrastructure Overhead: Significant infrastructure for small projects
• Debugging: Distributed ML workflows are hard to debug

Architecture Diagram Description¶

High-Level Architecture¶

flowchart LR
  D[Data sources] --> ING[Ingestion + validation]
  ING --> FS[Feature store]
  FS --> TR[Training]
  TR --> EVAL[Eval / metrics]
  EVAL --> REG[Model registry]
  REG --> DEPLOY[Deploy:<br/>online / batch / edge]
  DEPLOY --> MON[Monitoring:<br/>quality + drift]
  MON -. retrain trigger .-> TR

[Data Sources] --> [Data Ingestion] --> [Feature Store]
                                              |
                                    [Feature Engineering]
                                              |
                        [Experiment Tracking] -- [Model Training]
                                              |
                                    [Model Evaluation]
                                              |
                                    [Model Registry]
                                              |
                          +-------------------+-------------------+
                          |                   |                   |
                  [Real-time Serving]  [Batch Inference]  [Edge Deployment]
                          |                   |                   |
                  [Monitoring & Drift Detection]
                          |
                  [Retraining Trigger]

MLOps Maturity Levels¶

Component Breakdown¶

Data Layer¶

Training Layer¶

Serving Layer¶

Monitoring Layer¶

AWS Implementation¶

Core Services¶

Architecture Pattern¶

• S3 stores raw data and training datasets
• SageMaker Processing jobs handle data preparation
• Feature Store provides online (DynamoDB) and offline (S3) feature serving
• SageMaker Training jobs run on managed GPU instances
• SageMaker Experiments tracks all training runs
• Model Registry manages model versions with approval workflow
• SageMaker Endpoints serve models with auto-scaling
• Model Monitor detects data quality, model quality, and bias drift

SageMaker Pipeline Example¶

from sagemaker.workflow.pipeline import Pipeline
from sagemaker.workflow.steps import ProcessingStep, TrainingStep

pipeline = Pipeline(
    name="ml-pipeline",
    steps=[
        processing_step,   # Data preparation
        training_step,     # Model training
        evaluation_step,   # Model evaluation
        condition_step,    # Check metrics threshold
        register_step,     # Register in model registry
        deploy_step        # Deploy to endpoint
    ]
)

Documentation: - Amazon SageMaker - SageMaker Pipelines - SageMaker Feature Store - SageMaker Model Monitor

Azure Implementation¶

Core Services¶

Architecture Pattern¶

• ADLS Gen2 or Blob Storage for data storage
• Azure ML Datasets for versioned training data
• Azure ML Compute Clusters with GPU VMs for training
• Managed Feature Store for feature engineering and serving
• MLflow integration for experiment tracking (built into Azure ML)
• Managed Online Endpoints for real-time inference with auto-scaling
• Batch Endpoints for scheduled large-scale predictions
• Data Collector captures inference data for monitoring

Documentation: - Azure Machine Learning - Azure ML Pipelines - Managed Feature Store - Managed Endpoints

GCP Implementation¶

Core Services¶

Architecture Pattern¶

• GCS stores raw data and training datasets
• BigQuery for feature computation and analysis
• Vertex AI Feature Store serves features for training and serving
• Vertex AI Training runs on managed GPU/TPU instances
• Vertex AI Experiments with TensorBoard integration
• Model Registry manages versions with deployment targets
• Vertex AI Prediction endpoints with traffic splitting for A/B tests
• Model Monitoring tracks feature skew and prediction drift

Vertex AI Pipeline Example¶

from kfp.v2 import dsl
from google_cloud_pipeline_components.v1.custom_job import CustomTrainingJobOp

@dsl.pipeline(name="ml-pipeline")
def pipeline():
    data_prep = data_preparation_op(input_data=...)
    training = CustomTrainingJobOp(
        display_name="model-training",
        worker_pool_specs=[...],
    )
    evaluation = model_evaluation_op(model=training.outputs["model"])
    deployment = model_deploy_op(model=evaluation.outputs["model"])

Documentation: - Vertex AI - Vertex AI Pipelines - Vertex AI Feature Store - Vertex AI Model Monitoring

MLOps Practices¶

Version Control¶

• Code: All training code, pipeline definitions, and configurations in Git
• Data: Version training datasets using DVC, Delta Lake, or platform tools
• Models: Version models in model registry with metadata
• Experiments: Track all experiment parameters and results
• Infrastructure: IaC for all ML infrastructure (Terraform, CloudFormation)

CI/CD for ML¶

Testing Strategies¶

• Unit Tests: Test feature engineering functions and data transformations
• Data Tests: Validate data schema, distributions, and quality
• Model Tests: Check model accuracy against baselines and test sets
• Integration Tests: Verify end-to-end pipeline execution
• Shadow Testing: Run new models alongside production without serving
• A/B Testing: Gradually route traffic to new model versions

Feature Stores¶

Online vs Offline Serving¶

Feature Store Benefits¶

• Consistency: Same features used in training and serving
• Reusability: Features shared across teams and models
• Discovery: Catalog of available features with documentation
• Time Travel: Access point-in-time correct features for training
• Monitoring: Track feature freshness and quality

Cross-Cloud Comparison¶

Model Monitoring¶

Types of Drift¶

Monitoring Strategy¶

1. Baseline: Capture statistics from training data as the baseline
2. Capture: Log all inference inputs and outputs
3. Analyze: Compare current data against baseline periodically
4. Alert: Notify when drift exceeds thresholds
5. Investigate: Root cause analysis of drift
6. Retrain: Trigger automated or manual retraining

Key Metrics to Monitor¶

• Model Performance: Accuracy, precision, recall, F1, AUC (when labels available)
• Prediction Distribution: Mean, variance, percentiles of predictions
• Feature Statistics: Mean, standard deviation, null rate, cardinality
• Operational Metrics: Latency, throughput, error rate, GPU utilization
• Business Metrics: Revenue impact, customer satisfaction, conversion rate

Cost Estimation¶

AWS (Monthly Estimate - Moderate ML Workload)¶

Azure (Monthly Estimate - Moderate ML Workload)¶

GCP (Monthly Estimate - Moderate ML Workload)¶

Production Checklist¶

Data Pipeline¶

• Data ingestion pipeline automated and scheduled
• Data validation checks implemented
• Data versioning in place
• Feature engineering pipeline automated
• Feature store configured (online and offline)
• Data quality monitoring active

Model Training¶

• Training pipeline automated with SageMaker/Azure ML/Vertex AI Pipelines
• Experiment tracking configured
• Hyperparameter tuning integrated
• Distributed training configured for large models
• Spot/preemptible instances used for cost savings
• Training reproducibility verified

Model Management¶

• Model registry configured with approval workflow
• Model versioning with metadata and lineage
• Model evaluation criteria defined (minimum metrics)
• Model documentation and model cards created
• Responsible AI checks (bias, fairness) implemented

Deployment¶

• Serving infrastructure deployed with auto-scaling
• A/B testing or canary deployment configured
• Rollback procedure tested
• Inference latency and throughput benchmarks met
• Shadow deployment for new models before full rollout

Monitoring and Operations¶

• Data drift detection configured
• Model performance monitoring active
• Alerting thresholds defined and tested
• Retraining triggers configured (scheduled or drift-based)
• Cost monitoring and optimization in place
• Incident response runbooks documented

Related Guides: - Data Pipeline ETL Architecture - Lakehouse Architecture - Service Comparison - AI/ML