Monitor your Computer Vision Model Performance Over Time
A model is trained on historical datasets, which are collected over a long period of time and often don’t reflect the current trends. When such models are deployed for computer vision tasks like detection, classification, and instance segmentation in industries such as healthcare, automotive, retail, and manufacturing, performance degradation occurs. This is because incoming requests have a different distribution compared to the training dataset. In our deep learning deployment service, we provide all the required features, including drift monitoring, inference optimization, and model selection, to handle and address model drift with ease.
Backed and Supported by
Drift Sample Visualization
- Visualize data distribution shifts affecting the latest computer Vision Transformers & CNN Models.
- Helps in collecting more samples using data labeling and annotation tools for retraining.
- Focus on varying distributions over time with data visualization and data versioning.

Dataset Creation & Model Retraining
- Understand reasons for performance degradation in AI inference.
- Easily create new datasets from real application inference samples using ML-Assisted Labeling.
- Follow the pipeline for model retraining and deployment using state-of-the-art models optimized for NVIDIA GPU and AWS, GCP, OCI compute.
