Data scientist
Systematically improve models
Eliminate the guesswork from the model improvement equation. Iteratively refine your projects' tests and rapidly identify the models' bottlenecks, get to their root causes, and solve the issues. Compare model versions side-by-side to ensure progress.
Incorporate state-of-the-art results
Upgrade your model development pipeline with state-of-the-art results from explainability, counterfactual analysis, synthetic data, and more.
Cultivate high-quality and representative datasets
Datasets are ever-evolving to represent the real world and prevent new failure modes. Strategically generate data to augment your training set and keep track of their versions, ensuring high data quality standards.
Communicate results with the team
It takes all hands on deck to build trustworthy models. Seamlessly collaborate with other data scientists, domain experts, engineers, analysts, and other stakeholders. Document your findings with powerful comments.
ML engineer
Keep track of model versions
Interact with and debug multiple model versions in a single place. Label model versions according to the deployment criteria and know which version to fall back to if there are issues in production.
Find tricky edge cases that can happen in production
Models in production need to interact with a long tail of edge cases. Systematically perturb the data to probe for unforeseen failure modes: from counterfactual analysis to assessing model prediction invariances.
Deploy with confidence
Ship with confidence anytime of the day using a wide variety of test types. Make sure that models are stress-tested early and that the errors are caught proactively rather than retroactively.
Compare models side-by-side
Go beyond aggregate metrics and make informed decisions when it comes to choosing models. Thoroughly compare different model versions side-by-side.
Product manager
Get an overview of roadmap progress
Keep track of progress in model development over time to make sure your team is on target to hit roadmap tests.
Evaluate models
Make sure that the offline progress is translated into online gains. Work with the team setting the adequate tests to tie back model development metrics to product and business metrics.
Interact with models
Seamlessly interact with different model versions to understand their strengths and weaknesses. No code or messy notebooks required.
Identify pain points
Define, communicate, and monitor guardrails around model development. Find tricky cases that could be filtered out and contact the technical team before the model is deployed in production.
Chief technology officer
Collaborate with engineers
Don't miss the forest for the trees. Collaborate with engineers on a high level to understand how model development on different projects affect the organization's tests.
Determine if business tests are being met
ML models are built to serve business needs. Make sure that the efforts in model development are aligned with the business tests.
Diagnose key product issues
Have a single workspace to seamlessly interact with different model versions. Identify gaps where the technology behind the product is failing and prioritize fixing them.
Assess and mitigate risk
Collaborate with the team in defining a project's tests and the safeguards needed around the model. Make sure that the models powering the product do not pose unanticipated risks.
Domain expert
Provide perspective on model requirements
Together with the development team, clearly define tests, expectations, and requirements for the data and the model.
Collaborate with engineers
Keep a tight collaboration loop with engineers and data scientists developing ML models. Provide the development team with domain knowledge to ensure continuous improvements.
Add context to sub-populations
Help define and diagnose critical data subpopulations in which the model needs to perform at its best.
Sanity check models before shipping
Seamlessly interact with the different model versions to make sure that the model behaviors are compatible with what is expected of them prior to shipping.
What others are saying
Debugging error cases is the highest leverage way to improve ML systems. Openlayer makes it easy to debug those cases and, more importantly, helps fix them as well. I highly recommend using it in all ML workflows
Gautam Kedia
Head of Fraud ML at Stripe
The Openlayer team deeply understands the challenges faced by the ML community. Their platform is the best way to streamline the evaluation and analysis of models to drive continuous improvement in AI.
Max Mullen
Founder of Instacart
Openlayer is building the critical infrastructure for the safe deployment of AI at planetary scale.
Guillermo Rauch
Founder & CEO of Vercel
Openlayer has been a valuable asset to our team. The platform's timeline feature is excellent for tracking progress, and collaborating has become effortless. This is a top-notch platform for gaining insights into ML models.
Rishabh Gupta
Lead Data Scientist at Zuma
I've witnessed first-hand the critical importance of error analysis in the world of machine learning. The Openlayer platform can save countless debugging hours and significantly improve model performance for data scientists worldwide.
Mark Belvedere
Data Science Director at Meta
Openlayer is a unique, data-centric ML solution that supports test-driven development and data quality analysis. This tackles a critical problem around ML data intelligence that only grows with the increased ubiquity of AI.
Astasia Myers
Enterprise Partner at Quiet Capital