MLOps and DevOps are both practices that aim to improve processes where you develop, deploy, and monitor software applications.
DevOps aims to bridge the gap between development and operations teams. DevOps helps ensure that code changes are automatically tested, integrated, and deployed to production efficiently and reliably. It promotes a culture of collaboration to achieve faster release cycles, improved application quality, and more efficient use of resources.
MLOps, on the other hand, is a set of best practices specifically designed for machine learning projects. While it can be relatively straightforward to deploy and integrate traditional software, ML models present unique challenges. They involve data collection, model training, validation, deployment, and continuous monitoring and retraining.
MLOps focuses on automating the ML lifecycle. It helps ensure that models are not just developed but also deployed, monitored, and retrained systematically and repeatedly. It brings DevOps principles to ML. MLOps results in faster deployment of ML models, better accuracy over time, and stronger assurance that they provide real business value.