Kubeflow

Kubeflow is an open-source machine learning (ML) platform designed to make deployments of ML workflows on Kubernetes simple, portable, and scalable. It provides a framework for building and deploying end-to-end ML pipelines, enabling data scientists and engineers to easily experiment, iterate, and manage ML models in production. Kubeflow abstracts away much of the underlying infrastructure complexity, allowing users to focus on developing and training their models. Kubeflow simplifies the process of deploying machine learning models across diverse environments, from local laptops to cloud platforms. It offers components for tasks such as data preprocessing, model training, hyperparameter tuning, model serving, and pipeline orchestration. By leveraging Kubernetes, Kubeflow enables users to scale their ML workloads on demand, improve resource utilization, and automate the deployment and management of ML applications. Main use cases include building and deploying ML-powered applications, streamlining ML workflows, and democratizing access to ML capabilities within organizations.