Hystax Launches Kiroframe: Dedicated Mlops Platform for Scalable AI Development

Hystax announces the release of Kiroframe — a standalone MLOps solution designed to streamline, automate, and scale machine learning development workflows.

What is Kiroframe?

Kiroframe is an advanced machine learning operations (MLOps) platform that empowers data science and engineering teams to automate, monitor, and optimize every stage of the machine learning (ML) lifecycle. It features:

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• Model profiling and training insights — Captures both internal and external metrics, highlights bottlenecks, and offers recommendations for optimal performance, hardware selection, and cost efficiency across environments.

• Dataset tracking & management — Provides transparent dataset exploration, version control, and experiment linkage, with visual dashboards to track splits, usage, and lineage. Users can view dataset provenance, relationships between datasets, and how they’ve been used across experiments.

• AI Leaderboards — Ranks model runs using a consistent evaluation protocol for apples-to-apples comparison of experiments.

• ML/AI artifact management — Automates tracking and versioning of model checkpoints, logs, and dependencies, ensuring full traceability.

• Shared environments & governance — Offers role-based access, usage quotas, and shared reproducible environments for team consistency.

Why Kiroframe?

Kiroframe offers a unified experience for model tracking, dataset management, and performance model profiling, enabling ML teams to collaborate more effectively and scale experimentation with confidence. The platform bridges the gap between technical performance and business efficiency in ML initiatives.

Availability & getting started

Kiroframe is now available as a SaaS solution, with a free 14‑day trial for new users. It integrates seamlessly with commonly used machine learning tools and platforms, including GitHub, Slack, Databricks, Kubeflow, TensorFlow, PyTorch, Jenkins, AWS, and others.

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