MLOps - Real-World Machine Learning Projects for Professional

Master the end-to-end MLOps lifecycle by working on real-world machine learning projects — from model development to deployment and monitoring.

In today’s fast-paced AI-driven world, data science alone isn’t enough — deploying and maintaining ML models in production is what truly drives impact. This course takes you beyond traditional machine learning by focusing on MLOps (Machine Learning Operations) — the crucial bridge between ML development and real-world application.

Through hands-on, real-world projects, you’ll learn how to design, deploy, monitor, and scale ML models using modern tools and best practices. From CI/CD pipelines to model monitoring and automated workflows, this course gives you the skills top employers demand.

Whether you’re a data scientist, ML engineer, software developer, or aspiring MLOps specialist — this course will prepare you for real challenges in production environments.

🔍 What You Will Learn:

✅ Build and deploy machine learning models with best MLOps practices
✅ Implement end-to-end ML pipelines using tools like MLflow, Docker, and Kubernetes
✅ Automate workflows using CI/CD tools (GitHub Actions, Jenkins, etc.)
✅ Monitor and retrain models in production
✅ Work on real-world projects in finance, healthcare, and e-commerce
✅ Use cloud services (AWS/GCP/Azure) for scalable deployments
✅ Manage data versioning and model reproducibility
✅ Understand ML lifecycle management and team collaboration strategies

💼 Projects You’ll Build:

• Customer Churn Prediction System (CI/CD + Docker + MLflow)

• Real-Time Fraud Detection API (FastAPI + AWS Lambda)

• Medical Diagnosis Model (Kubernetes + Model Monitoring)

• E-commerce Recommendation Engine (Pipeline Automation + GitOps)

🎓 Who This Course Is For:

• Machine Learning Engineers looking to take models to production

• Data Scientists transitioning into MLOps roles

• DevOps Engineers expanding into ML workflows

• Software Developers with ML exposure

• AI/ML Enthusiasts who want real-world deployment experience

🛠 Tools & Technologies Covered:

• MLflow, DVC, Airflow

• Docker, Kubernetes, FastAPI

• GitHub Actions, Jenkins

• AWS/GCP/Azure

• Prometheus, Grafana

• Python, Scikit-learn, TensorFlow, PyTorch