# A Scalable and Cloud-Native Hyperparameter Tuning System

arXiv: https://arxiv.org/abs/2006.02085 · HTML: https://mlsanity.com/paper/2006.02085/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration](https://mlsanity.com/paper/2205.01423/) (2022)
- [OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System](https://mlsanity.com/paper/2303.00501/) (2023)
- [CLAIMED, a visual and scalable component library for Trusted AI](https://mlsanity.com/paper/2103.03281/) (2021)
- [VirnyFlow: Optimizing ML Pipelines for Accuracy, Fairness, and Stability at Scale](https://mlsanity.com/paper/2506.01584/) (2025)

## Abstract (arXiv)

In this paper, we introduce Katib: a scalable, cloud-native, and production-ready hyperparameter tuning system that is agnostic of the underlying machine learning framework. Though there are multiple hyperparameter tuning systems available, this is the first one that caters to the needs of both users and administrators of the system. We present the motivation and design of the system and contrast it with existing hyperparameter tuning systems, especially in terms of multi-tenancy, scalability, fault-tolerance, and extensibility. It can be deployed on local machines, or hosted as a service in on-premise data centers, or in private/public clouds. We demonstrate the advantage of our system using experimental results as well as real-world, production use cases. Katib has active contributors from multiple companies and is open-sourced at https://github.com/kubeflow/katib under the Apache 2.0 license.