Scaling Machine Learning Pipelines in Cloud Infrastructures Using Kubernetes and Flyte

- Scaling machine learning (ML) pipelines is a critical challenge in modern cloud infrastructures due to the growing complexity of data processing, model training, and deployment workflows. Kubernetes, a container orchestration platform, provides robust capabilities for managing distributed workloads, enabling seamless scalability and fault tolerance for ML workflows. Flyte, an open-source workflow orchestration platform, enhances Kubernetes by providing specialized tools for managing complex ML pipelines with reproducibility and versioning. This paper explores the design and implementation of scalable ML pipelines leveraging Kubernetes and Flyte. It highlights the architecture of Flyte's workflow management on Kubernetes clusters, emphasizing key features such as task orchestration, data caching, and scalability. A practical framework is proposed to address common challenges, such as resource optimization, pipeline versioning, and integration with cloud-native services. Real-world use cases demonstrate how Kubernetes and Flyte together simplify the automation of ML pipeline execution, reduce operational overhead, and enable teams to focus on innovation. The study concludes by discussing best practices and future directions, including advanced scheduling strategies, support for heterogeneous cloud environments, and enhancing developer productivity in ML workflows. This work provides a comprehensive foundation for engineers and researchers aiming to scale ML pipelines effectively in cloud-native ecosystems.