MLOps, or Machine Learning Operations, is a set of practices that aims to streamline the process of developing, deploying, and maintaining machine learning models. It applies principles from DevOps, which is focused on software development and operations, to the unique challenges of machine learning. The goal of MLOps is to ensure that machine learning models are scalable, collaborative, and reproducible, which is essential for their successful implementation in production environments ¹².

The MLOps framework addresses several key aspects of machine learning projects, including model creation, training, tuning, and deployment. By adopting MLOps practices, organizations can improve the efficiency and reliability of their machine learning workflows, making it easier for data scientists and machine learning practitioners to collaborate and produce high-quality models ²⁴.

In summary, MLOps is crucial for integrating machine learning into business processes, ensuring that models can be effectively managed and maintained over time.