Kubeflow: Streamline your ML workflows
Running Kubeflow on Nebius offers a scalable and cost-effective platform for managing machine learning workflows by integrating Kubeflow’s powerful ML tools with Nebius’s robust infrastructure and services.
This combination enhances performance, simplifies deployment, provides customization and security benefits, and can be especially advantageous if Nebius’ regional availability aligns with your compliance and data residency needs.
Benefits of the platform itself
Kubeflow is an open-source platform that simplifies the deployment of machine learning workflows on Kubernetes, offering a comprehensive ML stack for seamless model development, training and serving.
Streamlined ML workflows
Manage end-to-end machine learning pipelines on Kubernetes with seamless integration of data preparation, model training and serving, consistent workflows across different environments and automated ML pipeline orchestration.
Kubernetes-native
Leverage the power and flexibility of Kubernetes for ML by dynamically scaling resources based on workload demands, utilizing Kubernetes` robust resource scheduling management and benefiting from its portability across cloud and on-premises environments.
Comprehensive ML toolkit
Access a wide range of ML tools and frameworks with built-in support for TensorFlow, PyTorch and other popular ML frameworks, Jupyter notebooks for interactive development and experimentation and MLflow for experiment tracking.
Portable and scalable
Deploy and scale ML workflows consistently across environments by running them on-premises, in the cloud, or in hybrid setups, easily scaling from development to production and ensuring reproducibility of ML models.
Flexibility and extensibility
Customize and extend the platform to fit your specific ML needs while still maintaining security inherited from Kubernetes.
Collaborative environment
Foster teamwork and knowledge sharing among data scientists and engineers with a centralized platform for ML projects and experiments, version control integration for code and shared Jupyter notebooks for collaborative development.
Production-ready serving
Simplify the transition from model development to production deployment with built-in model serving capabilities and monitoring, canary deployments for gradual rollouts and integration with popular serving frameworks like TensorFlow Serving.
Kubeflow in action: Transforming ML workflows
Data science and research
- Collaborative research projects with reproducible environments
- Large-scale data analysis and visualization
- Rapid prototyping and experimentation with ML models
Enterprise ML operations
- Automating ML pipelines from development to production
- Scaling ML infrastructure to meet business demands
- Maintaining consistency across multi-cloud and hybrid environments
- Ensuring enterprise-grade security with Kubernetes
Ready to revolutionize your ML workflows?
Deploy Kubeflow on Nebius and unlock the full potential of machine learning on Kubernetes.