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:
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Customer Churn Prediction System (CI/CD + Docker + MLflow)
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Real-Time Fraud Detection API (FastAPI + AWS Lambda)
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Medical Diagnosis Model (Kubernetes + Model Monitoring)
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E-commerce Recommendation Engine (Pipeline Automation + GitOps)
🎓 Who This Course Is For:
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Machine Learning Engineers looking to take models to production
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Data Scientists transitioning into MLOps roles
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DevOps Engineers expanding into ML workflows
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Software Developers with ML exposure
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AI/ML Enthusiasts who want real-world deployment experience
🛠 Tools & Technologies Covered:
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MLflow, DVC, Airflow
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Docker, Kubernetes, FastAPI
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GitHub Actions, Jenkins
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AWS/GCP/Azure
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Prometheus, Grafana
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Python, Scikit-learn, TensorFlow, PyTorch
Lessons:
