Full Explainability and Interpretability Deep Learning Platform
How It Works
The platform runs mathematical operations on each node in the neural network’s computational graph in order to extract indicators from all model feature maps and evaluate their contributions.
Tensorleap’s algorithms then construct the most informative latent space to explain the model’s interpretation, find clusters of samples, and more.
Guided Error Analysis
Detect true root causes and understand which experiments are needed and why
Clustering:
Automatically group failing samples with identical root causes including metrics to evaluate the model’s quality and generalization
Sample analysis:
Explore a model’s interpretation of each sample and identify main reasons for the prediction
Visualization engine:
View the model’s performance and breakdown across all sample characteristics and drill down into any specific population
Dataset Architecture
Design unbiased and balanced datasets to achieve practical distribution.
Scoring:
Measure the quality of your datasets in regards to variance, density, entropy, and balance
Clean-up:
Identify repetitive, redundant, ambiguous, and mislabeled data to understand which samples should be removed
Labeling prioritization:
Find the best samples for labeling to improve variance and achieve required representation of certain data that’s causing model failures
Deep Unit Testing
Ensure a problem was solved and no regression took place while scanning for unknown issues
Split test sets:
Create numerous unit tests by either clustering using model features or automatically running cross-search on sample characteristics, and then validate them all simultaneously
Guided selection:
Drill down on specific sample groups to define and track specific tests using a visualization dashboard
Automatic scan:
Run unsupervised analysis utilizing a model’s features to search for suspicious clusters and anomalies, without relying on user’s understanding of model’s behavior
Keep Your Team in the Loop
Share insights, findings, and results with your team
Pull requests:
Enable developers to quickly review changes with their related tests and understand the impact on model’s interpretation
Issues:
Automatically track and document the entire issue-resolution process including tests, references, related samples, and more
Reporting:
Build a culture of transparency and collaboration and quickly generate reports to track model evolution.
Tensorleap is a fully Kubernetes-based solution which runs on any cloud or on-premise infrastructure.
It allows for quick integration to any framework of your models and datasets and supports any type of structured or unstructured data (images, text, time-series, tabular, etc.)
