Kubeflow

Building scalable and reliable machine learning models is critical for cloud-native AI systems. Kubeflow provides a robust framework for orchestrating model development workflows. This article presents best practices for ML model development in Kubeflow Cloud-Native Systems, with a focus on Azure Kubernetes Service environ- ments. It explores strategies for optimizing cluster configuration, designing modular and reproducible training pipelines, and implementing effective model tracking and ver- sioning processes. Real-world case studies highlight practical applications of these techniques.

This article presents the main findings from an in-depth study of data preparation strategies using Kubeflow in cloud-native AI systems deployed on Azure Kubernetes Service. The results demonstrate that integrating Kubeflow Pipelines with Azure-native tools enables scalable and automated processing of large datasets, significantly improving training efficiency and model accuracy. The use of TensorFlow Data Validation proved effective in detecting schema anomalies and data drift, enhancing data reliability across iterative ML workflows.