From the course: MLOps Essentials: Model Deployment and Monitoring

Getting started with MLOps

From the course: MLOps Essentials: Model Deployment and Monitoring

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Getting started with MLOps

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- [Kumaran] More and more machine learning models are being deployed today, serving a variety of use cases for business and personal outcomes. The focus on ML management has given rise to the world of machine learning operations, or MLOps. My name is Kumaran Ponnambalam. In this course, I will introduce you to the essentials of MLOps for model deployment and monitoring. I will start off with options and best practices for model deployment. Then, I will cover various model serving patterns and considerations. Next, I will focus on monitoring ML models in production and the metrics that are important. I will discuss the basics of model drift and how to monitor them. Finally, I will briefly touch upon responsible AI and how it integrates into the MLOps framework. Students are expected to be familiar with general model building and deployment concepts, and are recommended to have prior experience in implementing machine learning. It is highly recommended to take the prerequisite course, MLOps Essentials: Model Development and Integration, before starting this course.

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