• views: 352
• date: 15 December 2025
• posted by: 0nelove

Learn Production-Grade MLOps using DVC, MLFlow, AWS, Docker, Kubernetes, KServe, SageMaker and Kubeflow.
What you'll learn
Introduction to Machine Learning Operations (MLOps)
Transition from DevOps Engineers to MLOps Engineers
Machine Learning Basics for DevOps Engineers
Model Deployment and Monitoring in Production
End-to-End ML Pipeline Orchestration
Real-World MLOps Project
Requirements
Fundamental understanding of DevOps
Basic understanding of DevOps concepts like Docker, Kubernetes and CI/CD.
Description
MLOps Zero to Hero is a practical, hands-on course designed to help engineers understand how machine learning systems are built, deployed, and operated in real production environments. The course focuses on the real challenges teams face after a model is trained versioning data, tracking experiments, deploying models, scaling inference, and managing ML workloads reliably.You will start with the fundamentals of the ML lifecycle and gradually move into core MLOps practices. The course covers data and model versioning using DVC, experiment tracking with MLflow, and containerization using Docker. You will deploy models on Kubernetes, understand production-grade serving patterns, and implement Kubernetes-native inference using KServe.The course also introduces AWS-based MLOps workflows, including Amazon SageMaker, to help you understand how managed platforms are used in real organizations. You will further explore Kubeflow to learn how ML pipelines and training workloads are orchestrated in Kubernetes environments.Every concept is explained using simple examples and real-world workflows, with a strong emphasis on clarity and practical understanding rather than theory. By the end of the course, you will have a complete picture of how machine learning moves from experimentation to production — and the confidence to design, deploy, and operate MLOps systems in real projects.
Who this course is for
DevOps Engineers planning to transition to MLOps roles
Beginners curious about Model Deployments and Model Maintenence
Everyone who is curious about undertstanding how ML models are dealt at production level.



   
   
   

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