Machine Learning
Introduction to MLOps for Data Science
A part of continuous integration, continuous development, and continuous testing
What is MLOps?
If we break down the word itself, it is a combination of 2 words, machine learning, and operations. Where machine learning stands for model development or any kind of code development and operations means production and deployment of code.
A more technical definition of MLOps is a set of principles and practices to standardize and streamline the machine learning lifecycle management.
Well, it is not a new technology or tool but rather a culture with a set of principles, guidelines defined in a machine learning world to seamlessly integrate/automate the development phase with the operational development phase.
It is an iterative incremental process where data scientists, data engineers, and operations worlds collaborate to build, automate, test, and monitor the machine learning pipelines like a Dev-ops project.
We can say, MLops is inspired by Dev-ops and based on Dev-ops principles of continuous integration, continuous delivery, and additional continuous training applied to machine learning processes for faster development and deployment of models…