AI-Driven MLOps Pipeline with End-to-End Automation

Introduction

Machine Learning models are powerful, but deploying them at scale while ensuring automation, reproducibility, and performance monitoring is a challenge. This project aims to build an AI-Driven MLOps Pipeline that seamlessly integrates Kubernetes, Terraform, CI/CD, and cloud services to automate the entire ML lifecycle.

Project Goals

  • Automate ML workflows using Kubeflow.
  • Deploy ML models on Kubernetes for scalability.
  • Use Terraform for infrastructure automation.
  • Implement CI/CD to enable continuous deployment of ML models.
  • Monitor models using Prometheus, Grafana, and MLflow.

Technology Stack

Category Tools & Services
Cloud Platform AWS (EKS, S3, Lambda, SageMaker)
Infrastructure as Code Terraform
MLOps Orchestration Kubeflow, MLflow
Containerization & Deployment Docker, Kubernetes
CI/CD GitHub Actions, ArgoCD
Monitoring Prometheus, Grafana
Programming Python

System Architecture

1. Data Ingestion & Preprocessing

  • Data is uploaded to AWS S3.
  • AWS Lambda preprocesses the data before model training.

[Insert a flowchart showing data movement]

2. Model Training & Experiment Tracking

  • Model training takes place on AWS SageMaker.
  • MLflow is used for experiment tracking.
  • Trained models are stored in S3.

[Insert a diagram of the training pipeline]

3. Model Deployment & Serving

  • Models are deployed in Kubeflow Serving.
  • Kubernetes (EKS) handles containerized deployments.

4. CI/CD Pipeline

  • GitHub Actions & ArgoCD automate the ML pipeline.
  • The pipeline ensures continuous integration and deployment.

5. Monitoring & Auto-Healing

  • Prometheus & Grafana track model performance.
  • Kubernetes HPA enables auto-scaling and self-healing.

[Insert a Grafana dashboard screenshot]

Step-by-Step Implementation

Phase 1: Infrastructure Setup

  • Terraform provisions AWS services:
    • S3 for data storage
    • EKS for Kubernetes cluster
    • SageMaker for training
    • Lambda for automation
  • Deploy Kubeflow on EKS.

Phase 2: ML Model Development

  • Implement a sample ML model (e.g., fraud detection, NLP, or image classification).
  • Use MLflow for tracking experiments.
  • Store trained models in S3.

Phase 3: CI/CD Pipeline

  • Configure GitHub Actions to automate:
    • Data validation
    • Model training
    • Model testing
  • Deploy models using ArgoCD & Kubernetes.

Phase 4: Model Monitoring & Optimization

  • Implement Prometheus & Grafana for monitoring.
  • Enable Kubernetes Horizontal Pod Autoscaler (HPA) for auto-scaling.
  • Automate retraining when performance degrades.

Conclusion

This project showcases advanced MLOps, Cloud DevOps, Infrastructure as Code (IaC), and CI/CD techniques. By implementing a scalable, automated, and monitored ML pipeline, we ensure efficiency and reliability in real-world AI applications.

[Insert final architecture diagram summarizing the pipeline]

Next Steps

We will proceed with Terraform-based infrastructure setup as the first step of implementation.