MLOps platform to track, automate, and scale your AI development

Enable seamless ML/AI development with unified model profiling, dataset management, and team workflows — driven by MLOps flow automation

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ML/AI model profiling

Dataset tracking

ML/AI Leaderboards

AI development cost management

ML/AI artifacts

ML/AI model profiling

Kiroframe provides in-depth insights into ML/AI model training by profiling both internal and external performance metrics. Identify resource bottlenecks, optimize hyperparameter configurations, and enhance training efficiency across both on-premises and cloud environments. Get actionable recommendations on compute usage, instance selection, and performance tuning to reduce costs and accelerate experiments. Track key KPIs across teams and ensure every training run drives better outcomes.

Dataset tracking and management

Kiroframe helps ML/AI teams explore, version, and manage datasets with full transparency across experiments. Link datasets to specific model runs, monitor usage, and compare training outcomes using built-in leaderboards and performance metrics. With dataset descriptions, train/validation/test splits, and lineage tracking, teams can easily share and reuse high-quality data. Visual dashboards highlight infrastructure usage and the impact on cost, enabling smarter dataset-driven decisions.

ML/AI Leaderboards

ML/AI Leaderboards provide versioning of model training experiments and rank ML tasks based on metrics. Kiroframes’ Evaluation Protocol, with a set of rules by which candidates are compared, ensures that trained models are tested consistently and enforces an apples-to-apples comparison.

Supported technologies

ML/AI development cost management

Track and manage cloud spending across ML/AI experiments, pipelines, and environments with complete cost transparency. Kiroframe enables engineering and data science teams to detect inefficiencies, control budget usage, and get actionable recommendations, such as rightsizing, adopting spot instances, and migrating instance families. Empower your team to innovate while maintaining strict cost control and accountability across projects.

ML/AI artifacts

Kiroframe enables teams to capture, store, and version all critical ML artifacts — including datasets, model checkpoints, experiment logs, and dependencies. Every artifact is automatically linked to its corresponding task, model, and dataset, ensuring complete traceability and reproducibility across the entire ML workflow. With structured version control and seamless artifact organization, Kiroframe supports collaboration, auditability, and the confident delivery of reliable, explainable ML models.

Team and organizational use of shared ML environments

Kiroframe enables seamless collaboration through shared ML environments that are consistent, reproducible, and easy to manage. Teams can access pre-configured, versioned environments with standardized dependencies, ensuring smooth transitions between experimentation and production. Role-based access, usage logs, and resource quotas help prevent conflicts, optimize infrastructure utilization, and maintain complete control over environment usage.

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News & Reports

Slide deck

MLOps open source platform

A full description of OptScale as an MLOps open source platform.

Enhance the ML process in your company with OptScale capabilities, including

ML/AI Leaderboards
Experiment tracking
Hyperparameter tuning
Dataset and model versioning
Cloud cost optimization

How-tos

How to use OptScale to optimize RI/SP usage for ML/AI teams

Find out how to:

enhance RI/SP utilization by ML/AI teams with OptScale
see RI/SP coverage
get recommendations for optimal RI/SP usage

Article

Why MLOps matters

Bridging the gap between Machine Learning and Operations, we’ll cover in this article:

The driving factors for MLOps
The overlapping issues between MLOps and DevOps
The unique challenges in MLOps compared to DevOps
The integral parts of an MLOps structure

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