This comprehensive MLOps course provides a complete roadmap for understanding how machine learning models are developed, deployed, managed, and monitored in production environments. Designed for beginners, it combines machine learning concepts with modern DevOps and cloud engineering practices.
The course starts by introducing machine learning fundamentals, including different types of machine learning models and the tools commonly used in AI development. It then covers essential software engineering technologies such as Python Flask, Git, GitHub, and Jenkins, which are widely used in production workflows.
A significant portion of the course focuses on MLOps infrastructure and automation. You will learn how Docker containers and Kubernetes orchestration help deploy and scale machine learning applications efficiently. Monitoring concepts are also introduced through Prometheus, enabling visibility into system and model performance.
The training further explores major cloud platforms including AWS, Microsoft Azure, and Google Cloud AI services. Topics include Azure DevOps pipelines, Azure Machine Learning, AWS Machine Learning services, and deploying machine learning models using Amazon SageMaker.
In addition to deployment workflows, the course explains CI/CD practices for machine learning systems, helping bridge the gap between data science and operations teams.
By the end of this course, you will understand the complete MLOps ecosystem and gain the knowledge needed to build scalable, automated, and production-ready machine learning solutions.
