AI infrastructure management for scalable ML operations
Kiroframe helps you fix the real problems of scaling ML
Limited visibility into infrastructure and runtime performance
Difficult to monitor model metrics and performance in real time
Too many manual steps in model training
Slow model rollout and unstable performance
Kiroframe helps teams scale machine learning in enterprise environments. The platform monitors ML infrastructure, optimizes resource usage, and brings together all ML operations — from training to deployment — in one place.
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How Kiroframe optimizes your ML infrastructure
Monitor Everything
Full-stack monitoring of compute, storage, pipelines, and runtimes.
Track model performance and data quality in real-time.
Centralized Data Storage
Cloud storage allows easy access and sharing of datasets across teams. This promotes collaboration and ensures that everyone is working with the most up-to-date data.
Use Artifacts
Use Artifacts page to share specific artifacts with team members, facilitating easier collaboration and ensuring consistency across the project.
Helpful tips
Use the task section to track and manage your machine learning experiments. Tasks let you visualize, search for, and compare ML runs and access run metadata for analysis.
Examine the models section to keep track of your machine learning models. Models are used to make predictions or decisions based on data. This can help you make informed decisions about resource allocation and identify opportunities for optimization.
Connect integrations to automate workflows and foster better teamwork across platforms.
Simplifies the hyperparameter tuning process by automatically launching multiple experiments.
Experiments can be executed in parallel on multiple instances, significantly speeding up the process.
Optimize and monitor your AI infrastructure in minutes
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