Domino Data Lab | Company Profile
Company Directory for Domino Data Lab
Domino Data Lab
Overview
Based in San Francisco and founded in 2013, this organization offers an Enterprise AI platform designed to help large organizations build, operate, and scale their data science and machine learning initiatives. The platform addresses significant challenges in data science, including collaboration, reproducibility, and operationalization of machine learning models.
The company provides an Enterprise MLOps platform that supports the entire machine learning lifecycle, encompassing experimentation, deployment, and monitoring. Its solutions feature collaboration and governance tools, scalability across diverse environments, and support for open-source tools. The platform is utilized in healthcare, agriculture, automotive, finance, and technology sectors to optimize decision-making, enhance productivity, and improve products. Notably, it serves over 20% of Fortune 100 and numerous Global 2000 companies, backed by prominent investors including Sequoia Capital and NVIDIA, and was recognized by Deloitte’s Technology Fast 500 for rapid growth.
Basic Information
| Industry | information technology & services |
|---|---|
| Founded | 2013 |
| Revenue | 22.6M |
| Headquarters | 548 Market Street, San Francisco, California, United States |
| Languages | English |
Contact Details
- Phone: +1 415-570-2425
- Website: domino.ai
- LinkedIn: linkedin.com/company/domino-data-lab
Key Focus Areas & Initiatives
- Analytics
- Machine learning
- Data analysis
- Predictive analytics
- Data science
- Data mining technology
- Big data analytics
- Enterprise software
- AI governance policies
- Model registry
- AI model validation workflows
- Model security
- Cloud infrastructure
- Operational efficiency
- Hyperparameter tuning
- Enterprise data access
- Multi-cloud orchestration
- Automation
- Data management
- AI governance workflows
- AI infrastructure automation
- Deep learning
- Automated model validation
- Model deployment in regulated industries
- Open ecosystem support
- Cost control in AI
- Regulatory compliance
- AI compliance
- Automated governance
- Data science collaboration
- Hybrid cloud deployment
- Model reproducibility workflows
- MLOps
- Collaboration tools
- Model drift detection
- Cost optimization
- AI deployment automation
- Model monitoring
- AI compliance automation
- AI risk management
- Hybrid multi-cloud
- Model deployment pipelines
- AI model lifecycle
- Enterprise AI security
- AI lifecycle automation
- Multi-cloud AI deployment
- Model version control in enterprise
- Model management
- Model deployment
- AI impact measurement tools
- Multi-cloud AI orchestration
- AI platform scalability
- Hybrid cloud AI
- Generative AI
- AI model explainability tools
- Model versioning
- AI model governance
- Open source ecosystem
- Cloud computing
- Data exploration
- AI security
- Real-time monitoring
- Comprehensive reproducibility
- Efficient computing
- Automated policy enforcement in AI
- Enterprise data management
- AI model audit trail
- Financial management
- Infrastructure automation
- AI transparency
- Resource optimization
- Data lifecycle management
- Model experimentation
- Distributed computing
- AI training
Technologies Used
- AI
- AdRoll
- Airtable
- Atlassian Cloud
- Backbone JS Library
- Bing Ads
- DNS Made Easy
- DoubleClick
- DoubleClick Conversion
- DoubleClick Floodlight
- Eventbrite
- Facebook Custom Audiences
- Facebook Login (Connect)
- Facebook Widget
- Gmail
- Google Apps
- Google Dynamic Remarketing
- Google Tag Manager
- Hotjar
- Hubspot
- Linkedin Marketing Solutions
- Microsoft Office 365
- Mixpanel
- Mobile Friendly
- MongoDB
- Python
- Rackspace MailGun
- React
- Remote
- Segment.io
- Snowflake
- Typeform
- Vimeo
- Visual Website Optimizer
- WP Engine
- YouTube
- Zendesk