Fine-tune your computer vision data curation to match the real world, make your dataset more comprehensive, and improve the model's accuracy.
Improving the performance of machine learning models can be frustrating and inefficient. Without being able to properly identify why the model is performing poorly, teams are often forced to add large volumes of data to their training dataset with the hope of it solving the issue. This is an expensive and timely procedure and often results in bloated, large, and inefficient datasets. By proving insights on the specific areas where your model is performing poorly, manot allows you to optimize the process of improving your model, and decrease the feedback loop from months to hours.
manot is a model performance monitoring platform for computer vision models. Creating computer vision models that work well in the lab or in pre-production environments is not enough to ensure that your model will function well in dynamic and complex real-world domains. manot allows you to monitor your model's performance in the production environment to evaluate where your model is failing.
We detect outliers and data distribution drifts as the model is exposed to new data. Data distribution drifts are one of the top reasons why models don’t operate as well in production as they do in pre-production. Model monitoring through manot allows you to detect data drifts and deal with them to resolve performance-related issues. Our outlier detection system provides you with insights into which samples of data your model is likely to perform poorly on. manot also suggests which samples of data need to be added to your training dataset, or relabeled, in order to improve the overall accuracy of your model.
Improving the performance of machine learning models can be frustrating and inefficient. Without being able to properly identify why the model is performing poorly, teams are often forced to add large volumes of data to their training dataset with the hope of it solving the issue. This is an expensive and timely procedure and often results in bloated, large, and inefficient datasets. By proving insights on the specific areas where your model is performing poorly, manot allows you to optimize the process of improving your model, and decrease the feedback loop from months to hours.
manot is a model performance monitoring platform for computer vision models. Creating computer vision models that work well in the lab or in pre-production environments is not enough to ensure that your model will function well in dynamic and complex real-world domains. manot allows you to monitor your model's performance in the production environment to evaluate where your model is failing.
We detect outliers and data distribution drifts as the model is exposed to new data. Data distribution drifts are one of the top reasons why models don’t operate as well in production as they do in pre-production. Model monitoring through manot allows you to detect data drifts and deal with them to resolve performance-related issues. Our outlier detection system provides you with insights into which samples of data your model is likely to perform poorly on. manot also suggests which samples of data need to be added to your training dataset, or relabeled, in order to improve the overall accuracy of your model.
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