Deepchecks Overview
Deepchecks is an open-source package for machine learning validation. The company's open-source solution is a Python library for data scientists and machine-learning engineers. The package includes extensive test suites for machine learning models and data, built in a way that's flexible, extendable, and editable. Suites are composed of checks. Each check contains outputs to display in a notebook and/or conditions with a pass/fail output. Conditions can be added or removed from a check; checks can be edited or added/removed to a suite; suites can be created from scratch or forked from an existing suite.
Cumulative Funding Raised Over Time ($)
Latest News
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Deepchecks raises funding and launches open source validation platform for ML models
Deepchecks, a Machine Learning Operations (MLOps) company, has released an open-source platform for continuously validating ML models. The company has also secured $14 million in seed funding, with Alpha Wave Ventures leading the investment round. The platform aims to establish an ML safety and predictability standard, bridging the gap between research and production. Deepchecks enables developers to attain visibility and confidence throughout the entire ML lifecycle, offering tools for development, deployment, and production operations. The companys enterprise version provides advanced collaboration and security features. The platform has already been used by companies like AWS, Booking, and Wix. Deepchecks aims to enhance AI model testing through validation and monitoring, addressing the challenges of the ML markets projected rapid growth. The companys open-source tools have gained traction across the tech industry.
InvestmentExpand
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Testing and validating machine learning models and data with Deepchecks
The article provides an overview of Deepchecks, a Python package used for testing and validating machine learning models and data. Deepchecks helps assess the performance, integrity, and feasibility of models and data. It offers checks for data distribution, model performance evaluation, and data integrity. The package provides a suite of checks that generate interpretable reports on the issues associated with the data and models. Using Deepchecks, machine learning engineers and developers can easily interpret flaws and generate reliable outcomes from their models. The article also mentions the DriftScore evaluation metric and the potential future expansion of Deepchecks to support more data types and models.
Customers
| Sector | Business Software |
Funding
| Total funding | $14M |
| Last funding | $14M |
| Stage | Seed |
| Rounds | 2 |
| Investors | 3 |
Team Members
3
Employees: 11-50
Web & Social Links
| Website | www.deepchecks.com |
| Careers | Careers page |
Locations
Yehuda Burla St 19, Tel Aviv-Yafo, Israel
Photos & Videos
3 item(s)