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
Shared environments management
ML/AI
Leaderboards
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 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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