MLOps and Continuous ML Delivery Pipelines

Surging rates of machine learning deployment in real-world applications have highlighted the need for improved systems built to support large-scale, dependable and trustworthy AI solutions. Extending DevOps practices, MLOps focuses on ML lifecycle management to address the demands of handling data versions, re-training models, managing governance and delivering ML services continually. This article thoroughly examines how to use MLOps to deploy, monitor and develop machine learning models in many different work settings. We observe through history how MLOps progressed from managing models manually to the present use of automated and professional systems. key elements such as data engineering, model training using automation, validation workflow, the process to deploy the model and post-deployment monitoring are carefully looked at. This domain specializes in issues that distinguish ML systems from traditional software systems. It means a change in the inputs, change in the data and the need for frequent updates to the model. We discuss how to manage model governance effectively, paying attention to checking versions and ethical approval, as well as the importance of automating infrastructure for complex ML tasks. It also focuses on future changes in MLOps such as adopting foundation models, using AutoML for better pipeline design and focusing on edge deployment strategies. It brings together industrial processes, academic findings and case study reviews to guide researchers and practitioners in making machine learning useful for many users. The findings underscore MLOps’ pivotal role in enabling sustainable, trustworthy, and agile AI systems across industries.