About Kiroframe#

Kiroframe is a comprehensive MLOps platform designed to help data science and engineering teams track, automate, and scale AI development by streamlining model profiling, dataset management, and collaborative workflows across the entire machine learning lifecycle.

The platform 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.

It is 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, Gitlab, Jenkins, Slack, AWS, Alibaba, Azure, Google Cloud, and others.

Primary capabilities#

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.

Privacy Policy#

We respect your company’s privacy and recognize that your machine learning models, data, and code are among your most valuable assets. This policy outlines the data our MLOps product accesses and stores to provide you with a robust platform for building, deploying, and managing your machine learning lifecycle. To deliver a comprehensive MLOps solution, we require access to the information produced by your ML experiments and (optionally) access to the part of your development and cloud environments. This access allows us to automate workflows, track experiments, and provide insights into your model performance. The required permissions are detailed in our documentation and are designed to be minimally intrusive while enabling the full functionality of the product.

We utilize this access to:

Track and version your experiments: We log parameters, metrics, and artifacts associated with each training run to ensure reproducibility and to help you compare results.
Manage your models: We provide a central registry for your trained models, allowing you to version, stage, and manage their lifecycle from development to production.
Track the linage of your datasets: We store datasets metadata and relations between datasets, experiments and models to ensure full transparency of the datasets production and usage.
The following information is stored internally to deliver the best value of the product: - ML Models and Datasets metadata, version history and relations between them - Experiment parameters, metrics, and artifacts metadata - Pipeline configurations and run history - Securely stored credentials for third-party integrations (e.g., cloud providers, code repositories) - List of users in the product

WE DO NOT SHARE YOUR DATA WITH ANY THIRD PARTY OR STORE ANY NETWORK INFORMATION LIKE IP ADDRESSES, SECURITY GROUPS, OR VPC CONFIGURATION.