This Advanced MLOps tutorial covers how to design, build, and deploy production-grade machine learning systems at scale.
In this video, you will learn:
• End-to-end MLOps architecture
• CI/CD for Machine Learning
• Model versioning and experiment tracking
• Production model deployment strategies
• Monitoring, drift detection, and retraining pipelines
• Scalable ML infrastructure design
• Real-world MLOps best practices
We go beyond theory and implement practical workflows used in modern AI teams.
If you want to become an ML Engineer, AI Engineer, or MLOps Engineer, this deep dive will give you real production-level understanding.
In this video, you will learn:
• End-to-end MLOps architecture • CI/CD for Machine Learning • Model versioning and experiment tracking • Production model deployment strategies • Monitoring, drift detection, and retraining pipelines • Scalable ML infrastructure design • Real-world MLOps best practices
We go beyond theory and implement practical workflows used in modern AI teams.
If you want to become an ML Engineer, AI Engineer, or MLOps Engineer, this deep dive will give you real production-level understanding.
Tech stack covered: MLflow, Airflow, Kubernetes, Docker, CI/CD pipelines, cloud deployment, monitoring systems.