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Internal and external performance training metrics
Flexible hyperparameter tuning
Metrics under control
Kiroframe enables teams to conduct comprehensive profiling of machine learning models by collecting both internal (e.g., training accuracy, data loss, iterations) and external (e.g., compute resource utilization, cloud cost) metrics.
Track model behavior at every stage — from preprocessing to deployment — and gain deep visibility into where performance dips occur.
The system maps each training run to the underlying infrastructure and execution context, providing your team with the data they need to optimize both the model and the environment in which it runs.
Kiroframe supports profiling of models across multiple hyperparameter collections using runsets, helping teams identify the most effective configuration for a given task.
You can group several training runs into one task and compare outcomes under different combinations of learning rate, batch size, number of epochs, accuracy, and more.
The platform integrates with scheduling tools and AWS infrastructure to launch these runs in parallel using Reserved or Spot Instances, and to automatically monitor performance.
Every training run in Kiroframe logs standardized metrics — including accuracy, loss, epoch count, and iteration count — along with your custom KPIs.
Metrics are aggregated using configurable functions, and you can set target thresholds and performance tendencies to evaluate success.
You can compare these results across model versions and link them to leaderboard protocols using champion/candidate evaluation.
