Open-source technologies for building ML platforms

Managing ML tasks individually by deploying standalone ML containers in a Kubernetes cluster can become challenging when dealing with a large number of users and workloads. To address this complexity and enable efficient scaling, many open-source technologies have emerged as viable solutions. These technologies, including Kubeflow, MLflow, Seldon Core, GitHub, Feast, and Airflow, provide comprehensive support for building data science environments, model training services, model inference services, and ML workflow automation.

Before delving into the technical details, let’s first explore why numerous organizations opt for open-source technologies to construct their ML platforms. For many, the appeal lies in the ability to tailor the platform to specific organizational needs and workflows, with open standards and interoperable components preventing vendor lock-in and allowing the flexibility to adopt new technologies over...