MLOps Mastery: Streamlining Machine Learning Lifecycle Management

Abhijit Joshi

Abstract: The advent of MLOps has revolutionized the field of machine learning, enabling efficient lifecycle management from development to deployment. This paper examines the principles and practices of MLOps, highlighting the tools, frameworks, and methodologies that streamline machine learning operations. By integrating development and operational workflows, MLOps ensures continuous integration, delivery, and monitoring of machine learning models, thereby enhancing their scalability, reliability, and productivity. The paper includes detailed case studies showcasing successful MLOps implementations across various industries, demonstrating the tangible benefits of adopting MLOps practices in real - world scenarios. Key tools within the Databricks ecosystem, such as the Databricks compute layer, Databricks Storage Layer and Unity Catalog, Databricks workflows for orchestration, and the Autoloader mechanism from Databricks, are explored. Additionally, the paper discusses the integration of these tools with GitHub, Apache Airflow, DBT, and S3 Cloud object storage to provide comprehensive insights into the MLOps ecosystem.

Keywords: MLOps, Machine Learning Lifecycle, Databricks, Continuous Integration, Continuous Deployment, Model Orchestration, Model Monitoring, Data Transformation, Apache Airflow, GitHub, S3 Cloud Storage, DBT

How to Cite?: Abhijit Joshi, "MLOps Mastery: Streamlining Machine Learning Lifecycle Management", Volume 13 Issue 1, January 2024, International Journal of Science and Research (IJSR), Pages: 1807-1815, https://www.ijsr.net/getabstract.php?paperid=SR24628132316, DOI: https://dx.doi.org/10.21275/SR24628132316