Leveraging Databricks Pipelines for Machine Learning Model Deployment and Monitoring

Abstract

This article explores the use of Databricks pipelines for deploying and monitoring machine learning models efficiently. Databricks provides a scalable and collaborative environment that integrates seamlessly with MLflow for tracking and managing the machine learning lifecycle. The study examines the various stages of machine learning pipeline development, including automated data preprocessing, model training, hyperparameter optimization, and deployment. Additionally, it highlights the significance of continuous monitoring through Databricks’ built-in metrics and logging features, ensuring real-time performance tracking and model drift detection. By leveraging cloud-based platforms such as Azure and AWS, along with powerful tools like Delta Lake and Databricks MLflow, organizations can enhance the reliability and scalability of their ML workflows.