Cloud ML Pipeline
Automated machine learning pipeline for model training, validation, and deployment on AWS
Project Overview
The Cloud ML Pipeline is a comprehensive solution for automating machine learning workflows in the cloud. Built with modern DevOps practices and cloud-native technologies, it provides a complete framework for training, validating, and deploying ML models at scale.
The pipeline integrates with AWS services including EKS for Kubernetes orchestration, S3 for data storage, and EC2 for compute resources. It uses MLflow for experiment tracking and model registry, Apache Airflow for workflow orchestration, and Terraform for infrastructure management.
Key achievements include reducing model deployment time by 90%, improving training efficiency by 60%, and achieving 99.8% uptime with automatic scaling and cost optimization.
Project Details
Technologies
Key Features
Automated Training
End-to-end ML pipeline with automated data preprocessing, model training, and validation
Version Control
Complete model versioning and experiment tracking with MLflow integration
Cloud-Native
Built on AWS with Kubernetes orchestration for scalable and reliable deployment
Monitoring & Logging
Comprehensive monitoring of model performance and system health in production
Security First
Secure model serving with authentication, authorization, and data encryption
Auto-scaling
Automatic scaling based on demand with cost optimization and resource management
Challenges & Solutions
Pipeline Orchestration
Coordinating complex ML workflows across multiple services and environments
Solution:
Implemented Apache Airflow for workflow orchestration with custom operators
Model Versioning
Managing multiple model versions and ensuring reproducibility across environments
Solution:
Integrated MLflow for experiment tracking and model registry with automated versioning
Infrastructure as Code
Deploying and managing complex cloud infrastructure reliably
Solution:
Used Terraform for infrastructure provisioning and Kubernetes for container orchestration
Cost Optimization
Balancing performance requirements with cloud infrastructure costs
Solution:
Implemented auto-scaling policies and spot instance usage for cost-effective deployment
Performance Metrics
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