A production-grade, self-healing MLOps pipeline for banking fraud detection. Built on AWS with SageMaker, MLflow, and GitHub Actions; featuring automated retraining, data drift monitoring, and fairness auditing.

A production-ready, serverless machine learning inference pipeline built using AWS CDK, Lambda, API Gateway, and SageMaker, with full CI/CD automation via GitHub Actions. Built to demonstrate mid-level AWS and MLOps engineering skills.

Production-grade MLOps pipelines with real-world ML and NLP projects.Covers MLflow, DVC, Docker,Airflow,GitHub Actions, AWS SageMaker, HuggingFace, and monitoring with Grafana and PostgreSQL. Model development CI/CD pipelines,experiment tracking,data versioning,workflow orchestration,cloud deployment,and monitoring using modern MLOps tools and AWS.

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Mlops with DVC ,MLflow,AWS Sagemaker,Docker and Github Actions CICD

Repo for storing the code to implement MLOps using AWS Sagemaker and Github Actions.

MLOps Level 1 - Continuous Training Pipeline using Airflow, MLflow, Github Action, AWS Sagemaker

End-to-end MLOps project for store sales forecasting using AWS SageMaker, CI/CD with GitHub Actions, and automated model deployment.

End-to-end AWS MLOps platform for Spanish utility customer churn prediction, retention recommendations, and Streamlit dashboard serving, built with Terraform, Step Functions, SageMaker, and GitHub Actions CI/CD

This repository deals with end-to-end mlops lifecycle utilizing Git, GitHub, AWS Sagemaker, GitHub Actions for various tasks related to Infra set up, CI/CD set up and final ML model deployment.

End-to-end MLOps pipeline for real-time credit card fraud detection with automated training, evaluation, and deployment to AWS SageMaker. Built with Random Forest, MLflow experiment tracking, and GitHub Actions CI/CD.

End-to-end customer churn prediction MLOps project built on AWS. Trains a machine learning model using AWS Sagemaker to predict customer churn, deploys it as a FastAPI microservice on AWS ECS Fargate, containerized with Docker, and automates the pipeline using GitHub Actions CI/CD.

AWS-native platform with Retrieval-Augmented Generation (RAG), parameter-efficient fine-tuning (PEFT), built with full MLOps and IaC. Features FastAPI, Docker, AWS CDK, ECS Fargate, SageMaker, S3, Secrets Manager, CloudWatch, CI/CD with GitHub Actions, robust security, monitoring, automation for enterprise AI.

A full-stack MLOps pipeline for real-time grape disease detection powered by AWS SageMaker and integrated with Lambda, API Gateway, and Terraform. Implements CI/CD workflows using GitHub Actions for seamless model deployment and infrastructure automation.

Materials from my “Introduction to MLOps” workshop - a rapid, hands-on guide for senior engineers on moving from Jupyter notebooks to large-scale, automated ML on AWS. We cover data & model versioning with DVC, continuous training via GitHub Actions and SageMaker, containerised inference with ECR/ECS, and production monitoring through CloudWatch.