More and more AI models are being integrated into everyday life, transforming how people interact with data. As a result, developers are expanding the AI capabilities of their applications, making Machine Learning Operations (MLOps) increasingly important. MLOps encompasses practices, tools, and technology that allow companies to effectively manage the entire lifecycle of an ML application in a production environment.
Kubeflow, an open-source platform built on top of Kubernetes, is a prominent tool in the MLOps ecosystem. In this series of articles, the significance of MLOps in today’s IT landscape is emphasized, with a focus on introducing Kubeflow as the foundation for MLOps best practices. This will assist data scientists in getting started with MLOps.
Dr. Pavol Bauer, a Senior Data Scientist and Product Manager at T-Systems, specializes in implementing the AI lifecycle from data collection to deployment in the cloud. Dr. Sebastian Lehrig leads MLOps with Open Source at IBM, striving to provide optimized solutions for IBM infrastructure in an efficient, secure, and reliable manner.
MLOps aims to bridge the gap between data scientists and operations teams, as data scientists develop AI models while operations teams are responsible for deploying and managing those models in production. To achieve this, MLOps combines AI-specific workflows with DevOps principles, enabling the development and deployment of software based on best practices. MLOps ensures that teams can reliably and scalability bring models from development to production, continuously improving them along the way.
Kubeflow was initially initiated by Google, IBM, and AWS in 2017 and has since evolved into a collaborative project involving various organizations and individuals. Unlike MLOps toolkits offered by major hyperscalers, Kubeflow can be utilized in private or hybrid Kubernetes-based clouds, potentially eliminating vendor lock-in when transitioning to a different cloud infrastructure. Additionally, Kubeflow can be accessed as a managed service from different providers.
By adopting MLOps practices and leveraging the capabilities of Kubeflow, organizations can accelerate and enhance the utilization of AI models in production environments. This ensures faster and more effective deployment of models, contributing to improved outcomes and advancements in the field of AI.