AI/ML Solution Architecture
Building scalable, secure, and efficient AI/ML infrastructures on AWS and Databricks
Why You Need an AI/ML Solution Architect
Infrastructure Expertise
Design scalable and cost-effective cloud architectures that support your AI/ML workloads
Data Strategy
Implement robust data pipelines and storage solutions optimized for machine learning workflows
ML Ops Excellence
Establish efficient ML operations practices for model development, deployment, and monitoring
Security & Compliance
Ensure your AI solutions meet security requirements and industry regulations
Performance Optimization
Optimize infrastructure and workflows for maximum performance and cost efficiency
Integration Expertise
Seamlessly integrate AI/ML solutions with existing systems and workflows
Platform Solutions
AWS AI/ML Stack
- SageMaker Ecosystem
Complete ML platform for building, training, and deploying models at scale
- AI Services
Pre-trained AI services for common use cases like computer vision and NLP
- Infrastructure
Scalable compute resources with GPU support and automated scaling
- Integration
Seamless integration with AWS services for end-to-end ML workflows
Databricks Lakehouse
- Unified Analytics
Combined data warehousing and ML platform for simplified workflows
- MLflow Integration
Built-in experiment tracking and model management capabilities
- Collaborative Environment
Interactive notebooks and workspace for data scientists and engineers
- Delta Lake
Reliable data lake architecture for ML data management
Example ML Solution Architectures
AWS ML Pipeline Architecture
Reference Architecture:
Components & Flow:
- Data Ingestion
- S3 for raw data storage
- AWS Glue for data cataloging
- AWS Lambda for data preprocessing triggers
- Data Processing
- AWS Glue ETL jobs for data transformation
- Amazon EMR for distributed processing
- Feature Store in SageMaker
- Model Development
- SageMaker Studio for development environment
- SageMaker Training Jobs for model training
- SageMaker Experiments for experiment tracking
- Deployment & Serving
- SageMaker Endpoints for real-time inference
- Lambda functions for API integration
- API Gateway for REST endpoint exposure
- Monitoring & Maintenance
- CloudWatch for metrics and logging
- SageMaker Model Monitor for drift detection
- EventBridge for automated retraining
Databricks Lakehouse ML Architecture
Reference Architecture:
Components & Flow:
- Data Management
- Delta Lake for data storage and versioning
- Auto Loader for streaming ingestion
- Unity Catalog for data governance
- Data Processing
- Spark SQL for data transformation
- Delta Live Tables for pipeline orchestration
- Feature Store for feature management
- Model Development
- Databricks Notebooks for development
- MLflow for experiment tracking
- AutoML for model optimization
- Model Serving
- Model Serving for real-time inference
- Batch inference using Spark
- Model Registry for version control
- Monitoring & Governance
- MLflow Model Monitoring
- Unity Catalog for model governance
- Workflow orchestration with Jobs
Hybrid ML Architecture (AWS + Databricks)
Reference Architecture:
This architecture demonstrates how AWS and Databricks can be integrated to leverage the best of both platforms:
- Data ingestion and storage using AWS services
- Data processing and ML training on Databricks
- Model deployment across both platforms
- Unified monitoring and governance
Key Architecture Considerations
Scalability
Design architectures that can handle growing data volumes and computational demands
Cost Optimization
Implement cost-effective solutions with appropriate resource utilization
Security
Ensure data protection and compliance throughout the ML lifecycle
Monitoring
Implement comprehensive monitoring for both infrastructure and model performance