From the course: MLOps Essentials: Model Development and Integration
Join today to access over 24,900 courses taught by industry experts.
Principles of MLOps
From the course: MLOps Essentials: Model Development and Integration
Principles of MLOps
- [Instructor] What are the principles based on which MLOps functions? The overall objective of MLOps is to create an optimal end-to-end machine learning workflow that integrates different teams, modules, and artifacts to continuously improve and deliver machine learning solutions. The principles of MLOps focuses on helping achieve this overall objective. This set of principles need to be kept as goals when adding any policy, process, tool, or workflow into the machine learning pipeline. To begin, the solution should be modular with well defined boundaries and ownership. This helps different artifacts to evolve simultaneously while enabling integration. The process should enable continuous development, modeling, integration and deployment. Automation is a key principle to achieve efficiencies and scale. MLOps should enable incremental development with quick time to market. The entire workflow should be managed…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.