Tensorleap is a deep-learning debugging and explainability platform that helps teams analyze model behavior, curate datasets, and monitor production drift.
What it is
Tensorleap is a deep-learning debugging and explainability platform built for teams developing neural networks in fields like robotics, autonomous vehicles, and healthcare. It bridges the gap between lab training and real-world production by providing deep visibility into model decision-making, rather than treating models as black boxes.
Key features
- Diagnose failure modes, domain gaps, and edge cases to find root causes
- Prioritize labeling efforts and prune redundant data to optimize datasets
- Receive guidance for hyper-parameters, loss functions, and targeted retraining subsets
- Monitor production environments for data drift and regressions with instant alerts
- Integrate with standard machine learning stacks including PyTorch, TensorFlow, and cloud storage providers
Who it's for
Tensorleap serves machine learning engineers, perception teams, and developers building neural networks where model failures result in real-world physical events.
Pricing
Check the official website for current pricing.
Use cases
- Identify root causes of deep-learning model failures
- Prioritize dataset labeling and prune data redundancy
- Detect drift and regressions in production environments
- Analyze neural network behavior and edge cases
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