Introduction to MLOps

MLOps serves as a multidisciplinary approach that merges the principles of DevOps, ModelOps, and DataOps to facilitate the end-to-end life cycle of ML projects. It aims to streamline the transition from model development to deployment, while also ensuring effective monitoring and management. In this framework, we have the following:

• DevOps: This focuses on the continuous integration and deployment of code, aiming for quicker releases and more reliable software
• ModelOps: This specializes in managing ML models, ensuring they are effectively trained, validated, and deployed
• DataOps: This deals with data management practices, encompassing everything from data collection and preprocessing to storage and analytics

MLOps improves the performance, stability, and long-term efficiency of ML systems. There are two primary risks that MLOps can help mitigate for your use case and industry:

• Technical risks: These result from poorly managed models...