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 ($)
—Dec 2019
$14.0MJun 2023
Latest News
growth-positive
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
growth-positive
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)
Deepchecks Business
Business models
B2B
Product stage
Released
Employees
11-50
Sector
Business SoftwareIT, R&D & Data SolutionsData Analysis & Decision SupportTarget Customer
Enterprise & Professional ServicesCore Technology
Artificial IntelligenceDeep LearningMachine LearningTags (5)
machine-learningmonitoringartificial-intelligencealert-systemdeep-learningSolutions
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{"title":"Financial Services, E-commerce, Retail","description":"Deepchecks provides an MLOps platform for continuous monitoring, validation, and explainability of machine learning models used in fraud detection, ensuring their performance, integrity, and reliability to minimize financial losses and improve operational efficiency.","items":[{"name":"Revenue Growth","value":4,"titleTooltip":"Revenue Growth & Profitability","meterTooltip":{"title":"Transformative / Strategic","content":"Fundamental, high-value enhancement with far-reaching strategic importance."}},{"name":"Operational Efficiency","value":4,"titleTooltip":"Operational Excellence & Efficiency","meterTooltip":{"title":"Transformative / Strategic","content":"Fundamental, high-value enhancement with far-reaching strategic importance."}},{"name":"Customer Acquisition","value":3,"titleTooltip":"Customer Acquisition, Satisfaction & Loyalty","meterTooltip":{"title":"Substantial / Core","content":"Clear, significant contribution; broad, tangible gains."}},{"name":"Risk Mitigation","value":5,"titleTooltip":"Risk Mitigation & Business Resilience","meterTooltip":{"title":"Disruptive / Paradigm-Shifting","content":"Revolutionary, industry-leading change that sets new benchmarks and norms."}},{"name":"Talent Development","value":3,"titleTooltip":"Talent Development & Employee Empowerment","meterTooltip":{"title":"Substantial / Core","content":"Clear, significant contribution; broad, tangible gains."}},{"name":"Agility & Innovation","value":4,"titleTooltip":"Strategic Agility & Innovation","meterTooltip":{"title":"Transformative / Strategic","content":"Fundamental, high-value enhancement with far-reaching strategic importance."}}]}
{"title":"Cross-industry (e.g., E-commerce, SaaS, Retail)","description":"Deepchecks' LTV (Customer Lifetime Value) use case provides tools for monitoring, validating, and ensuring the quality and fairness of machine learning models used to predict customer lifetime value, enabling data-driven customer strategies and optimized resource allocation.","items":[{"name":"Revenue Growth","value":4,"titleTooltip":"Revenue Growth & Profitability","meterTooltip":{"title":"Transformative / Strategic","content":"Fundamental, high-value enhancement with far-reaching strategic importance."}},{"name":"Operational Efficiency","value":3,"titleTooltip":"Operational Excellence & Efficiency","meterTooltip":{"title":"Substantial / Core","content":"Clear, significant contribution; broad, tangible gains."}},{"name":"Customer Acquisition","value":5,"titleTooltip":"Customer Acquisition, Satisfaction & Loyalty","meterTooltip":{"title":"Disruptive / Paradigm-Shifting","content":"Revolutionary, industry-leading change that sets new benchmarks and norms."}},{"name":"Risk Mitigation","value":4,"titleTooltip":"Risk Mitigation & Business Resilience","meterTooltip":{"title":"Transformative / Strategic","content":"Fundamental, high-value enhancement with far-reaching strategic importance."}},{"name":"Talent Development","value":2,"titleTooltip":"Talent Development & Employee Empowerment","meterTooltip":{"title":"Marginal / Localized","content":"Small, isolated improvement; measurable but not substantial."}},{"name":"Agility & Innovation","value":4,"titleTooltip":"Strategic Agility & Innovation","meterTooltip":{"title":"Transformative / Strategic","content":"Fundamental, high-value enhancement with far-reaching strategic importance."}}]}
Use Cases
NLPfraudLTVDeepchecks Financials
$14M
Total funding
Undisclosed
Capital raised
$14M
Last funding
Seed
Funding stage
2
Total rounds
3
Investors
Cumulative Funding Raised Over Time ($)
—Dec 2019
$14.0MJun 2023
Private Equity Funding
Seed
Jun 2023
$14M
Alpha Wave Ventures (Lead)
,
Grove Ventures, Hetz Ventures
Seed
Dec 2019
Undisclosed
Hetz Ventures (Lead)
,
Grove Ventures
Deepchecks Lifecycle
Cumulative Funding Raised Over Time
—Dec 2019
$14.0MJun 2023
All Events
Seed
Jun 2023
$14M
Alpha Wave Ventures (Lead)
,
Grove Ventures, Hetz Ventures
Seed
Dec 2019
Undisclosed
Hetz Ventures (Lead)
,
Grove Ventures
Deepchecks News
2 articles
growth-positive
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
growth-positive
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
Deepchecks Team
Employee Info
| Employees (range) | 11-50 |
| Exact count | 29 |
| Team members | 3 |
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Deepchecks Internal
Recent Updates
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Admin Info
| Confidence | 101/100 |
| Missing | markets |
| BI Verification | Yanina Wainscheinker |
| Registrar ID | 516104270 |
| Creator | Jenny Sotnik-Talisman |
| Creator email | genys30@yahoo.com |
| Last updater | Dana Arnon |
| Updater email | dana.arnon@mail.huji.ac.il |
| Last update | 2023-10-05T00:00:00.000Z |
| Created | 2020-01-14T00:00:00.000Z |
| Claimed | Yes |