Model Diagnostics
LatticeFlow platform fully comprehends your models for both custom and off-the-shelf architectures, providing deep insights into automated data and model improvement workflows.
Validate failure hypothesis
Do you have a couple of samples where you know your model underperforms?
Avoid expensive iteration cycles by checking custom hypotheses, by generalizing individual model failures and finding their root cause.
Auto-diagnose model blind spots
Do you know whether your model learned unwanted patterns that can cause costly errors in production?
Analyze internal model structure to uncover dataset subsets where the model systematically underperforms and take actions to fix them.
Understand model predictions
Use feature attribution to understand if your model is behaving as expected and relies on the right elements of the image to make accurate predictions.
Take model understanding to the next level by finding attribution patterns responsible for degraded model performance across the whole dataset, not just individual samples.
Model-guided data collection
Be smart about how new data is collected to minimize data collection costs and ensure the highest impact on model performance. Create data collection campaigns with your preferred labeling solution that is guided by your model blind spots and hard samples.
Benefits everyone on your team
All stakeholders in your organization including data analysts, domain experts, ML engineers can collaborate using a single source of truth in real-time to find and fix issues at scale.
Native model integration
We understand your custom AI models, plug in your own model or choose from 100+ out-of-the-box model integrations, all part of a secure on-premise deployment.
On top of your AI stack
LatticeFlow’s solution integrates with existing products for data labeling, model training, and model serving, complementing your existing AI infrastructure.
Always in full control
We got you covered with our Python SDK that provides all the advanced algorithms directly in your code. Ready to use for seamless CI/CD integration and custom automations.
What our customers say
Try LatticeFlow on your custom models
We know that it is painstakingly difficult to find and fix model issues. Whether it is resnet, yolov8 or deeplabv3, we have you coverted. Get to solution quicker, avoid pitfalls, and doing the same mistake over again.
Frequently
Asked Questions
Yes. We support both On-Prem or private cloud deployments.
Yes, we support native integration of your custom models.
We support models trained using PyTorch, Tensorflow 2, Keras, MMLab, FastAI, as well as many off-the-shelf architectures such as Yolo, Detectron2, DeepLabV3 and many more.
We provide off-the-shelf integration with popular foundational models, simply by selecting the framework and type of the model to integrate, while LatticeFlow does the rest. Similarly, any custom features or metadata can be integrated to enhance the analysis.
LatticeFlow natively supports a wide range of computer vision tasks including classification, regression, object detection and segmentation. Reach out to our Sales team to get a personalized demo.
We are working on Speech and NLP. Contact us to learn more.
