1 MLOps ML Life Cycle Challenges Associated with ML Life Cycle

MLOps fundamentals course, in this course we will learn about the MLOps fundamentals. MLOps, short for Machine Learning Operations, is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. This course covers the essential concepts and tools needed to understand and implement MLOps in your organization. You will learn about the lifecycle of machine learning models, including data preparation, model training, deployment, and monitoring. The course also delves into best practices for collaboration between data scientists, machine learning engineers, and operations teams to streamline workflows and improve productivity. We will explore key MLOps tools and frameworks such as Docker, Kubernetes, and CI/CD pipelines, and how they can be used to automate and scale your machine learning workflows. Additionally, the course will cover strategies for version control, reproducibility, and governance to ensure that your models are robust and compliant with industry standards. By the end of this course, you will have a solid understanding of the MLOps landscape and be equipped with the skills to implement these practices in your machine learning projects, leading to faster development cycles and more reliable model performance in production environments.