MLOps optimization: maximize the efficiency of your ML pipelines
Kiroframe helps you fix ML pipeline problems
No insights into how models perform in real-time
No clear view of what slows down your pipeline
Manual tuning and no easy way to scale
Kiroframe helps you see how models and pipelines perform in real time. With full observability, teams find bottlenecks faster and fix performance issues. Automated tuning and orchestration remove manual work and help your ML workflows scale smoothly.
Start optimizing your ML pipelines today
Get real-time insights, remove bottlenecks, and automate your ML workflows — all in one place.
How Kiroframe optimizes your ML infrastructure and enables optimization for machine learning
Track resource usage in ML pipelines
Use shared environments and task-level monitoring to track how much compute is used across training runs. This helps identify where performance can be improved and which steps require optimization.
Consistent monitoring
Regularly track and analyze your chosen task metrics over time to track progress, identify trends, and make informed decisions.
Manage Your Tasks
View detailed information about the task, including launch history and model versions. Adding leaderboards is also available with a single click.
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.
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.
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