Just as DevOps combines Development and Operations into a single integrated flow, MLOps unifies Machine Learning with Operations.
What is MLOps?
Table of Contents:
DevOps – MLOps:
Just as DevOps combines Development and Operations into a single integrated flow, MLOps unifies Machine Learning with Operations.
MLOps applies the DevOps methodology to the development and deployment of machine learning solutions.
What is MLOps?
MLOps is a collection of best practices for managing the development and use of machine learning artifacts via productive workflows, collaboration, and tracking.
So, MLOps is neither a product nor a methodology. Instead, MLOps is a set of processes and best practices for developing and deploying machine learning, aided by automation and tools.
MLOps merges data engineering and model development activities into the software engineering and deployment life cycle. It manages machine learning artifacts, data, and models in addition to the software engineering artifacts of code and records.
MLOPs Brief Overview:
In order to speed up time to market, MLOps enables continuous model creation and integration (Agile process).
MLOps covers deployment, servicing, monitoring, performance metrics, and generating comments.
Overall, through automation and tools that increase productivity, MLOps aids in the management of the Machine Learning processes.
So, in a nutshell, MLOps is a method of functionalizing models and placing them into production to make them beneficial.