End-to-End ML Platform on AWS
MLflow + SageMaker with Infrastructure as Code (AWS CDK)
Production-grade ML platform with experiment tracking, model registry, and SageMaker-based training & deployment, provisioned using AWS CDK. Designed for cross-industry applications in FinTech, HealthTech, Retail, and SaaS AI products.
Project Summary
Comprehensive Project Overview
Project Category
AI / MLOps / Cloud Platform Engineering
Industry/Domain
Cross-industry (FinTech, HealthTech, Retail, SaaS AI products)
Domain: Machine Learning Platforms / MLOps Infrastructure
Cloud Platform
AWS (Amazon Web Services)
Infrastructure as Code with AWS CDK
Key Technologies & Concepts
Core Technologies Used
Platform Keywords
Problem & Objective
What problem did this project solve?
Problems Solved
- Teams lack a production-grade ML platform to reliably track experiments, manage models, and deploy models at scale
- Missing proper infrastructure, security, and reproducibility in ML workflows
- No centralized system for the full ML lifecycle management
Primary Objectives
- Design and deploy a scalable ML platform on AWS that supports the full ML lifecycle
- Train, track, register, and deploy models using MLflow + SageMaker integration
- Provide infrastructure-as-code (IaC) deployment using AWS CDK for reproducibility
Solution & Architecture
Architectural Overview
Solution Overview
Built a cloud-native ML platform where MLflow runs on ECS Fargate (with RDS + S3), and SageMaker training jobs log experiments and models to the remote MLflow server. Infrastructure is fully provisioned using AWS CDK.
MLOps Platform Engineering + Production ML Systems: Pipelines using SageMaker training jobs integrated with MLflow experiment tracking and model registry; model promotion and deployment via MLflow → SageMaker endpoints.
CI/CD, containerisation and orchestration tools: Docker, ECS Fargate, ECR, Infrastructure as Code with AWS CDK (CI/CD-ready infra layer).
Key Components
- Reference Architecture: Cloud-native ML Platform Architecture (Infrastructure Layer: CDK → VPC → ECS Fargate → MLflow → RDS/S3)
- ML Lifecycle Layer: SageMaker → MLflow Tracking → Model Registry → SageMaker Endpoint
- Monitoring & Logging: MLflow experiment metrics, CloudWatch logs for ECS tasks, centralized artifact and model version tracking, reproducible runs
- YAML / IaC Mapping: AWS CDK synthesizes CloudFormation templates for full reproducibility of the ML platform infrastructure
Skills & Technologies Used
Technical Proficiency Demonstrated
Primary Skills
- MLOps Architecture (Advanced)
- Cloud Platform Engineering (Advanced)
- ML Systems Design (Advanced)
Secondary Tools / Frameworks
- MLflow
- SageMaker SDK
- Docker
- boto3
- MySQL (for RDS)
Programming Languages
- Python (Primary language for ML pipelines and CDK)
AWS Cloud & DevOps Tools
Challenges & Outcomes
Technical challenges and business value delivered
Key Technical Challenges
- Remote MLflow integration with SageMaker
- Custom MLflow containerization
- State management on stateless compute
- Secure secret handling
- Network/IAM wiring across services
How They Were Resolved
- Private MLflow service behind ALB (Application Load Balancer)
- Custom Docker images in ECR (Elastic Container Registry)
- RDS/S3 externalized state management
- IAM roles for ECS/SageMaker service communication
- Secrets Manager for secure credential handling
- AWS CDK for consistent infrastructure provisioning
Business & Production Value
Outcome
Delivered a reusable, production-grade ML platform architecture that can be extended to real business ML pipelines (fraud, forecasting, personalization, recommender systems).
Business Value
- Reduces ML deployment friction
- Enforces governance and reproducibility
- Supports multi-team ML workflows
- Enables faster AI-to-production cycles
- Cost-aware cloud design
Architecture & IaC Mapping
Architecture to AWS CDK construct mapping
| Architecture Component | AWS CDK / IaC Implementation |
|---|---|
| MLflow Server | ECS Fargate Service with custom Docker image |
| Experiment Tracking Backend | RDS MySQL instance for metadata storage |
| Artifact Storage | S3 bucket for model artifacts and experiment data |
| Model Training | SageMaker training jobs with MLflow integration |
| Model Registry | MLflow Model Registry on ECS with S3 backend |
| Model Deployment | SageMaker endpoints provisioned via MLflow |
| Networking | VPC with public/private subnets, security groups |
| Access Control | IAM roles and policies for least privilege access |
| Secrets Management | AWS Secrets Manager for database credentials |
| Load Balancing | Application Load Balancer (ALB) for MLflow service |
| Container Registry | ECR repositories for custom MLflow images |
| CI/CD Integration | AWS CDK for infrastructure as code deployment |
| Monitoring | CloudWatch logs and metrics for all services |
Platform Capabilities
Key features and functionalities
Experiment Tracking
- Centralized tracking of ML experiments
- Parameter and metric logging
- Artifact storage for models and datasets
- Reproducible experiment runs
- Comparison of different model versions
Model Management
- Versioned model registry
- Model staging and promotion workflows
- Automatic model versioning
- Model lineage and provenance tracking
- Collaborative model development
Model Deployment
- One-click deployment to SageMaker endpoints
- A/B testing capabilities
- Canary deployments
- Automatic scaling based on load
- Rollback to previous versions
Security & Governance
- VPC isolation for MLflow server
- IAM role-based access control
- Secrets management for credentials
- Encryption at rest and in transit
- Audit logging for all operations
Assets & References
Code, diagrams, study material
AWS CDK Code
Infrastructure as Code implementation for provisioning the complete ML platform on AWS.
View Code Repository