Open-source platform combining product analytics, session replay, A/B testing, and feature flags in one toolkit for product engineers.
Deepchecks Testing Package
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About Deepchecks Testing Package
Deepchecks Testing Package is an automated testing solution that helps software engineers save time, reduce errors, and improve the quality of their code. With Deepchecks, developers can create and run comprehensive and reliable tests for their codebase with ease. Our testing package works by running a set of pre-defined tests against code, and then reporting back any potential issues that might arise. The tests can be tailored to suit specific needs, and the results are presented in a simple, user-friendly format. Deepchecks is an ideal solution for developers of all skill levels, from novice to expert. Our package is easy to use and offers a wide range of features, including the ability to create complex tests, analyze test results, and take advantage of real-time feedback. With Deepchecks, developers can quickly identify and debug issues, ensuring the highest quality of their code. Our testing package is also cost-effective, with a subscription plan that fits any budget.
GitHub, Inc.
San Francisco, California, US · Founded 2008
- Founders
- Tom Preston-Werner, Chris Wanstrath, PJ Hyett, Scott Chacon
- Founded
- 2008
- Headquarters
- San Francisco, California, US
- Legal status
- Subsidiary of Microsoft (NASDAQ: MSFT)
Key features
- Create complex tests tailored to specific needs
- Receive real-time feedback on test results
- Debug and identify issues quickly and cost-effectively
Use cases
- Automated testing for software engineers
- Improving code quality through comprehensive testing
- Reducing errors and saving time with reliable tests
Pros
- Supports validation for tabular, NLP, and computer vision models
- Provides open-source testing, CI, and monitoring components for AI/ML workflows
- Enables continuous validation from research to production environments
- Offers both local and managed deployment options for monitoring
- Includes built-in checks and customizable suites for thorough testing
Cons
- Requires separate installation for NLP and computer vision submodules
- Monitoring setup involves additional steps for local deployment
- Documentation and onboarding may require time to navigate for new users
Frequently asked questions about Deepchecks Testing Package
What is Deepchecks Testing Package and what does it do?
Deepchecks is an open-source solution for validating AI and machine learning models and data throughout their lifecycle, from research to production. It provides tools for testing, continuous integration, and monitoring to ensure model reliability and performance.
Who should use Deepchecks?
Deepchecks is designed for data scientists, machine learning engineers, and software developers working with AI models who need to validate data quality, model performance, and detect issues before and after deployment.
How does Deepchecks work?
Deepchecks runs built-in and custom checks and suites for tabular, NLP, and computer vision data validation. It integrates with CI/CD pipelines for testing and offers monitoring capabilities for deployed models in production.
What are the components of Deepchecks?
Deepchecks includes Testing for validation, CI & Testing Management for collaboration and iteration, and Monitoring for tracking model behavior in production. Each component can be used independently or together.
How do I get started with Deepchecks?
Installation varies by component. For Testing, use pip install deepchecks -U --user. For Monitoring, use pip install deepchecks-installer followed by deepchecks-installer install-monitoring. Detailed instructions are available in the documentation.
Does Deepchecks support different types of data?
Yes, Deepchecks supports validation for tabular data, natural language processing (NLP), and computer vision (CV) through dedicated submodules and built-in checks tailored to each data type.
