In this MLOps tutorial, you will be introduced to MLOps. There will be an in-depth learning of the various practices that are involved in MLOps. You will get to know about the various issues that are present or can come up later when using the conventional Machine Learning model deployment method, sometimes also known as one-off deployments. You will learn how these issues can be resolved by making use of standardized MLOps practices. Additionally, you will also learn about the various personas involved in a Machine Learning project, their roles and responsibilities, and how they interact with each other and contribute to the project.
Audience
This MLOps tutorial is prepared for individuals who are interested in learning about MLOps and the practices involved in MLOps. This tutorial has been designed to take you from scratch to an advanced understanding of MLOps. After completing this tutorial, you will have a good understanding of MLOps and MLOps best practices and will be in a position to apply these MLOps practices on your own to Machine Learning projects.
Prerequisites
This tutorial is designed to get you started with MLOps and learn the best practices associated with it. Although it is not required to have any prior knowledge about Machine Learning or Software Development to complete this tutorial. However, it is helpful if you have a basic understanding of Machine Learning and Software Development. Any prior experience with Machine Learning and Machine Learning Development Lifecycle will help you understand and relate to the problems that led to the creation of MLOps. Additionally, since MLOps is an extension of DevOps to Machine Learning models and pipelines, any knowledge or prior understanding about Software Development Lifecycle or DevOps will be very helpful.
Let’s get to learning this new and exciting field of MLOps by starting this MLOps tutorial.