A production-grade MLOps pipeline for predicting telecom customer churn, featuring automated data preprocessing, ML model training, experiment tracking with MLflow, distributed training using PySpark, real-time inference via Kafka streaming, Airflow DAG orchestration, and Dockerized REST API deployment.

A self-taught AI/ML Engineer and Data Scientist showcase of 30+ production-ready and research projects in Machine Learning, Deep Learning, NLP, and MLOps. Built end-to-end pipelines, real-time systems, and deployable apps using tools like Docker, Kubernetes, Airflow, MLflow, TensorFlow, PyTorch, Hugging Face, and cloud platforms (GCP, AWS).

Complete MLOps learning project: synthetic data generation, S3 integration, Airflow DAG orchestration, distributed PySpark processing, and MLflow experiment tracking. Containerized environment showcasing end-to-end ML pipeline automation.

Enterprise-grade MLOps pipeline for predicting company bankruptcy. Automates data ingestion, model training, monitoring, retraining, and deployment on AWS with Airflow, MLflow, Terraform, and Streamlit. Handles imbalanced financial datasets and ensures continuous model reliability in production.

A simple MLOps pipeline, integrating tools like Airflow, MLflow, FastAPI, and Streamlit

Production-grade open source MLOps pipeline for enterprise data engineering and predictive modeling. Includes ETL, training, FastAPI deployment, Airflow orchestration, CI/CD, sample data, and MLflow integration. Ready for real business use and customization.

Repository demonstrates a complete end-to-end MLOps pipeline for building a weather forecasting model. By leveraging tools like DVC, Airflow, and MLFlow, we’ve created an automated, scalable, and reproducible workflow for collecting data, training models, and monitoring performance.

MLOps pipeline for customer churn case study - CodePro is an EdTech startup that had a phenomenal seed A funding round. It used the money to increase its brand awareness. As the marketing spend increased, it got several leads from different sources. Although it had spent significant money on acquiring customers, it had to be profitable in the long run to sustain the business. The major cost that the company is incurring is the customer acquisition cost (CAC). customer acquisition cost is required to be high in companies. But as their businesses grow, these companies start focussing on profitability. Many companies first offer their services for free or provide offers at the initial stages but later start charging customers for these services. For example, Google Pay used to provide many offers, and Reliance Jio in India offered free mobile data services for over a year. Once these brands were established and brand awareness was generated, these businesses started growing organically. At this point, they began charging customers. Businesses want to reduce their customer acquisition costs in the long run. There are many ways to do that. You will learn about these methods in the next segment.

Production-grade MLOps pipeline for customer churn prediction with automated training, validation, and serving. Built with Airflow, MLflow, MinIO, Evidently AI, and FastAPI.

This pipeline demonstrates a License Plate Detection task using Airflow for orchestration and MLflow for experiment tracking, as part of an MLOps practice course (CS317.P22).

End-to-end MLOps pipeline to fine-tune GPT-2 for creative ad copy generation. Uses Airflow for orchestration, Kubernetes for scalable execution, MLflow for experiment tracking and model registry, and S3 for artifact storage, with real-time monitoring via Prometheus and Grafana.

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MLOps pipeline for dental caries prediction using MLflow, Airflow, and Google Cloud Platform

End-to-end MLOps pipeline for loan default prediction using Airflow, MLflow, Vertex AI, Cloud Run, and Terraform.

🚀 A complete MLOps pipeline using DVC, MLflow, Airflow, OmegaConf, Optuna, and Docker for scalable machine learning workflows.

An end-to-end MLOps pipeline using MLFlow for model tracking, Airflow for automation, and Docker with Kubernetes for CI/CD and deployment.

Cloud-agnostic Airflow MLOps sandbox combining parallelized data pipelines with ML engineering tooling (MinIO, MLflow, Qdrant, RAPIDS) for end-to-end experimentation and observability.

End-to-end MLOps pipeline for ocean wave height prediction — from EDA and experiment tracking (MLflow) to model serving (FastAPI), containerization (Docker), and automated retraining (Airflow).

A sales forecasting system with a complete MLOps pipeline, using Apache Airflow for orchestration, MLflow for managing the model lifecycle, and FastAPI for serving inference.

School MLOps project developed at Albert School. The project implements a complete MLOps pipeline for image classification — from data ingestion and model training to deployment and monitoring. Technologies: FastAPI · MinIO · Airflow · MLflow · Docker · Kubernetes · GitHub Actions.

🚀 A 180-Day “AI & MLOps Zero to Hero” journey — transitioning from DevOps to MLOps. Hands-on projects with MLflow, DVC, Airflow, FastAPI, Kubeflow, KServe, and AWS SageMaker. Includes daily progress, automation scripts, CI/CD, and real-world cloud-native MLOps pipelines.

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.

Customer churn prediction MLOps pipeline - Logistic Regression, Random Forest, and XGBoost trained on Telco dataset, tracked with MLflow, versioned with DVC, orchestrated via Airflow, and deployed as a FastAPI REST API with Docker.

MLOps platform for intelligent document processing and validation. Includes OCR, data pipelines, model training, MLflow tracking, Airflow orchestration, and model serving via Seldon Core. Designed for scalable document recognition and classification in enterprise environments

An end-to-end MLOps pipeline for network intrusion detection using Airflow, Docker, and MLflow. CyberFlow automates data ingestion, drift detection, A/B model testing, and deployment, ensuring real-time monitoring and high accuracy.

MLOps is the practice of building automated pipelines to continuously train, evaluate, and deploy Machine Learning (ML) models. It's similar to the CI/CD principles used in traditional software development. Tools like MLflow, Kubeflow, or Airflow are highly beneficial for implementing MLOps.

MLOps Dandelion Classifier is an end-to-end MLOps project that trains, tracks, serves, and monitors a PyTorch ResNet18 model to classify images of dandelions vs grass. The stack includes Airflow pipelines for data ingestion and training, MinIO for S3-compatible storage, MLflow for experiment tracking, FastAPI for online inference, Streamlit for UI

Build scalable MLOps pipelines with Git, Docker, and CI/CD integration. Implement MLFlow and DVC for model versioning and experiment tracking. Deploy end-to-end ML models with AWS SageMaker and Huggingface. Automate ETL pipelines and ML workflows using Apache Airflow and Astro. Monitor ML systems using Grafana and PostgreSQL for real-time insights.

MLOps pipeline for Acute Lymphoblastic Leukemia detection from microscopic blood smear images. Includes data preprocessing with Apache Airflow, model tracking via MLflow, FastAPI deployment, and monitoring with Prometheus and Grafana. This is done as final project work for big data lab, Spring 2024, IITM.

This project is an end-to-end MLOps pipeline for License Plate Detection using the YOLOv8 object detection model. The pipeline is orchestrated with Apache Airflow and integrates MLflow for comprehensive experiment tracking, model versioning, and performance logging. This project also includes a real-time License Plate Detection API built using Fast