Domino Data Lab

San Francisco, CA, United States  All United States companies → — Founded 2013
Clinical AI Platform Developer Platform / API Healthcare Analytics / BI
Cardiology Internal Medicine Oncology
Drug Discovery & Research Population Health Analytics
Series G — $224M-$228M
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Products

Domino.ai (Real-World Evidence Platform)
Domino Data Lab
Drug Discovery & Research
Domino.ai provides a platform for high-impact science by accelerating insights, improving consistency, and ensuring traceability from data to decision in real-world evidence generation.
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About Domino Data Lab

Domino Data Lab is an Enterprise MLOps (Machine Learning Operations) platform that empowers highly regulated organizations, including those in the life sciences and healthcare sectors, to build, deploy, and manage AI at scale. The platform is designed to accelerate research, streamline model development, and enhance collaboration among data science teams. For physicians and researchers, this means a more efficient and governed environment for leveraging AI in areas such as drug discovery and development, multi-omics data processing, enhanced diagnostics, and preventative care.

The platform centralizes AI operations and knowledge, providing features like integrated version control, shared notebooks, and access to diverse datasets. This facilitates reproducibility, standardization, and reusability of scientific research and data analytics, which is critical in drug R&D. Domino Data Lab’s focus on governance and compliance ensures that AI systems and applications can be tracked, reviewed, and validated, addressing the stringent requirements of the healthcare industry.

By providing a robust and open infrastructure, Domino Data Lab helps healthcare organizations move AI projects from experimentation to production with speed and reliability, while also reducing costs and managing risks. This enables life sciences leaders to invent new drugs and more efficient crops, ultimately aiming to improve healthcare outcomes and positively impact millions of lives.

Focus Areas

Enterprise MLOps AI/ML Infrastructure Data Science Platform AI Governance Model Development Model Deployment Collaboration Reproducibility Life Sciences AI Financial Services AI Public Sector AI

Business Intelligence

Key InvestorsSequoia Capital, Great Hill Partners, Coatue Management, NVIDIA, Snowflake, 3VC, AllegisCyber Capital, Citi Ventures, DCM, Defy Partners, DNX Ventures, Graph Ventures, Highland Capital Partners, Mucker Capital, Owl Rock Capital Partners, Work-Bench, Zetta Venture Partners, UBS
PartnershipsAppsilon, NVIDIA, UCB, Johnson & Johnson
TechnologyEnterprise MLOps platform, cloud-native (AWS, Azure, GCP), containerized environments (Docker, Kubernetes), supports Python, R, SAS, MATLAB, TensorFlow, PyTorch

What Physicians Need to Know

Enterprise MLOps
Domino Data Lab provides an MLOps platform that centralizes infrastructure, streamlines model development, and enables collaboration across diverse environments. It integrates DevOps principles into the model lifecycle, offering version control, automated pipelines, and CI/CD practices for consistent, scalable model development and deployment. The platform orchestrates the full model lifecycle with speed, safety, and flexibility, helping enterprises reduce the cost and time-to-market for mission-critical AI use cases.
AI/ML Infrastructure
Domino offers an open and flexible infrastructure that scales with data science needs, supporting seamless integration with cloud platforms (like AWS, EKS, AKS, GKE) and on-premise environments. It provides self-service access to elastic compute resources, including CPUs and powerful GPU clusters, with IT guardrails and cost controls. Domino is Kubernetes-native and can be deployed on Amazon EKS for ease of management across hybrid environments.
Data Science Platform
Domino Data Lab is a unified platform for building, deploying, and managing AI models efficiently, providing a central hub for AI operations and knowledge. It offers a collaborative workspace with integrated version control, shared notebooks, and access to datasets, allowing data scientists to use their preferred languages and tools (Python, R, SAS, MATLAB, Jupyter, VS Code, RStudio). The platform accelerates workflows with high-performance compute resources and pre-configured environments.
AI Governance
Domino provides a comprehensive system of rules, standards, policies, and controls for the ethical development, deployment, operation, and decommissioning of AI systems. It ensures compliance with internal standards and regulations through smart version control, audit trails, and detailed logging to track changes to models, data, and code. Domino automates policy enforcement, evidence collection, and compliance monitoring within MLOps workflows, mitigating AI-related risks. It supports continuous AI governance, tracking models from proposal to retirement, including traditional ML, GenAI, third-party vendor algorithms, and spreadsheet models.
Model Development
Domino enables data scientists to develop models and analytics solutions using their preferred tools and libraries. It supports experimentation and iteration with various algorithms and parameters, allowing quick iteration and tracking of results. The platform provides interactive workspaces for data preparation and exploration, with visualization capabilities throughout the data science workflow.
Model Deployment
Domino supports scalable and reliable model deployment, streamlining MLOps workflows that automate deployment, versioning, and monitoring. It facilitates continuous monitoring of models in production, detecting issues like data drift or model degradation early on, and integrates with existing CI/CD pipelines. Automated workflows can trigger retraining of models based on performance metrics or data changes, with options to roll back to previous versions.
Collaboration
Domino enhances collaboration through integrated version control, shared notebooks, and access to datasets, allowing teams to develop and share insights without duplicating efforts. It provides a shared environment where data scientists can access code, data, and analyses created by other team members, promoting knowledge sharing. The platform also enables collaboration between data science teams and other stakeholders, such as business analysts and IT professionals.
Reproducibility
Domino ensures that every model, experiment, and result can be easily reproduced by storing all relevant artifacts, from data and code to model configurations, in a centralized repository. This is critical for auditability, review, and compliance, especially in regulated industries. The platform automatically captures and versions code, data, environments, and results, making it easy to revisit and replicate past projects.
Life Sciences AI
Domino helps accelerate drug discovery and clinical development with FDA-ready, reproducible AI, enabling faster breakthroughs with compliance built in. Six of the top 10 pharmaceutical companies use Domino to power AI and analytics across discovery, development, and manufacturing. The platform supports the modernization of Statistical Computing Environments (SCEs) and facilitates the use of large language models (LLMs) in discovering and developing new therapies.
Financial Services AI
Domino delivers trusted AI for fraud detection, risk modeling, and regulatory reporting, with transparency and governance at every step. It helps financial institutions optimize customer experience, enhance operational efficiency, and accelerate data decision-making. Domino Governance modernizes Model Risk Management (MRM) processes, providing a single system of record that adapts to regulations and guarantees AI reproducibility for audits.
Public Sector AI
Domino enables government agencies to integrate AI into their missions rapidly, safely, and cost-effectively. It supports the full AI lifecycle across GovCloud, on-prem, and air-gapped environments, addressing challenges like siloed tools and strict security requirements. The platform helps government officials and public sector leaders meet the critical need for AI adoption, emphasizing responsible AI, risk management, and increased AI integration.
Physician Tip

For physicians, Domino Data Lab's platform stands out by enabling the accelerated development and deployment of AI models in life sciences, which can lead to faster breakthroughs in drug discovery and clinical development. The emphasis on reproducibility and governance ensures that AI models used in healthcare are FDA-ready, auditable, and compliant with regulations, fostering trust in AI-driven insights. The platform's ability to integrate diverse data sources and tools means that physicians and researchers can leverage a wide range of data for personalized care and predictive analytics, moving healthcare towards a more proactive and preventative model. Furthermore, the collaborative environment allows for seamless teamwork among data scientists, researchers, and clinical stakeholders, accelerating the translation of AI models into actionable insights for patient care.

Domino Data Lab is built as an open platform that integrates with a wide range of data sources (e.g., Amazon S3, Amazon Redshift), IDEs (Jupyter Notebooks, JupyterLab, RStudio, VS Code, MATLAB, SAS), tools, libraries (Python, R, TensorFlow, PyTorch), and external repositories (Git, GitHub, MLflow, Sagemaker). Its extensible architecture allows companies to securely use additional open-source and proprietary packages.

Products by Domino Data Lab

1 product in the directory

Domino.ai (Real-World Evidence Platform)
Domino Data Lab
Drug Discovery & Research
Domino.ai provides a platform for high-impact science by accelerating insights, improving consistency, and ensuring traceability from data to decision in real-world evidence generation.
Subscription

What the Web Says

Domino Data Lab offers an Enterprise AI Platform designed to accelerate analytics and machine learning initiatives, providing tools for collaboration, version control, and reproducibility. The platform is generally well-regarded for its flexibility, extensibility, and ability to integrate with various cloud providers and programming languages. While product reviews are largely positive, employer reviews present a more mixed picture, highlighting strong benefits in some areas but also concerns regarding compensation and career progression.

Overall: Mixed

Strengths

  • Flexible and extensible platform, easily integrated with other solutions and multiple cloud providers.
  • Strong support for various programming languages (Python, R, SAS, Matlab) and open-source tooling.
  • Facilitates collaboration, version control, and reproducibility for data science projects.
  • Accelerates model deployment and MLOps integration, leading to faster time to production.
  • Offers strong enterprise security, governance, and auditing capabilities for regulated AI teams.
  • Positive employee benefits including comprehensive healthcare, flexible PTO, and remote work options.

Limitations

  • Can have a steep learning curve for new users and beginners, especially for generative AI implementation.
  • Complex implementation process for larger companies and potential integration challenges with third-party applications.
  • Customer support and training documentation could be improved.
  • Some employee concerns regarding stagnant pay, limited progression outside of promotions, and lack of 401(k) employer match.
  • Parental leave experiences are sometimes perceived as average despite competitive policies.
  • UI consistency and navigation can be challenging for broader user adoption.

Based on reviews from: G2, Comparably, Indeed.com, Reddit, Gartner Peer Insights, Built In, RFP.wiki

Last updated: 2026-08-24

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Press & Coverage

Futurum
Domino Data Lab: From MLOps Platform to Governed AI Application Factory - Futurum
Domino Data Lab is repositioning itself from an enterprise MLOps platform to a governed AI application 'factory,' emphasizing governance as a key differentiator for regulated enterprises. The company's Rev 2026 event highlighted this strategic pivot and new capabilities like App Hub and integrated coding assistants, targeting general availability in Q3 2026.
2026-07
PR Newswire
Domino Data Lab and Appsilon Partner to Speed AI to Production for Life Sciences - PR Newswire
Domino Data Lab announced a collaboration with Appsilon to accelerate AI to production for life sciences, providing a validated path from R and Python development to regulated production environments. This partnership aims to address the challenges life sciences organizations face in operationalizing AI due to a lack of validated infrastructure.
2026-06
Domino Data Lab
Domino Unveils New Capabilities to Take AI From Model to Mission-Critical Application
At its annual Rev conference, Domino Data Lab unveiled new capabilities for its Enterprise AI Platform, enabling regulated enterprises to build, scale, and govern AI-powered applications across the full application lifecycle. These new features, including App Hub and integrated coding assistants, are designed to help organizations move AI projects from demos to mission-critical business operations.
2026-05
PR Newswire
Domino Data Lab Joins U.S. Department of Energy's Genesis Mission Consortium to Accelerate AI-Driven Scientific Discovery - PR Newswire
Domino Data Lab has joined the U.S. Department of Energy's (DOE) Genesis Mission Consortium to accelerate AI-driven scientific discovery. Domino will contribute its expertise in balancing AI innovation and risk mitigation to help double American scientific productivity over the next decade.
2026-08
Domino Data Lab Blog
AI Automation Challenges in Regulated Industries - Domino Data Lab
This article discusses the unique challenges of AI automation in regulated industries like healthcare, financial services, pharma, and government, emphasizing the need for provable controls, traceability, and accountability. Domino Data Lab positions its enterprise platform as purpose-built to address these challenges with secure infrastructure, centralized governance, and deep observability.
2026-02
UCB
UCB and Domino Data Lab Collaborate to Modernize Statistical Computing Environment in Life Sciences
UCB and Domino Data Lab announced a strategic collaboration to modernize a Statistical Computing Environment (SCE) for the life sciences industry. This initiative aims to create a unified, scalable, and flexible platform to meet the evolving demands of clinical research, regulatory compliance, and efficient data analysis.
2025-05
Life Sciences Global News
Domino Data Lab Launches Automated Quality Control for Clinical Programming
Domino Data Lab launched SCE QC, an automated, audit-ready quality control solution designed to streamline statistical programming and ensure compliance for life sciences organizations. This new capability integrates QC tracking directly into the statistical computing environment, enhancing traceability and audit readiness.
2025-11
PR Newswire
Domino Data Lab Secures $100 Million Funding to Help Every Company Become Model-Driven with Enterprise MLOps - PR Newswire
Domino Data Lab announced a $100 million Series F funding round led by Great Hill Partners, with participation from NVIDIA and existing investors. This funding brings their total to $228 million and will be used to accelerate growth and innovation in their Enterprise MLOps platform.
2021-10

Frequently Asked Questions

Domino Data Lab provides an Enterprise MLOps platform that helps healthcare organizations accelerate the development, deployment, and management of AI/ML models. It offers a centralized environment for data scientists and researchers, enabling them to collaborate on projects, ensure reproducibility, and streamline the entire model lifecycle, which is crucial for sensitive healthcare data and regulatory compliance.
Domino Data Lab's platform incorporates features for AI Governance, allowing healthcare organizations to maintain an auditable trail of model development, data usage, and decision-making. This helps in demonstrating compliance with regulations like HIPAA and GDPR by providing transparency and control over AI models, ensuring data privacy and ethical AI practices.
Domino Data Lab is designed to be an open and extensible platform, capable of integrating with various existing IT infrastructures. While specific EHR integrations would depend on the individual system and API availability, Domino's platform can connect to diverse data sources and leverage existing computational resources, allowing for seamless incorporation into a healthcare organization's data ecosystem.
Domino Data Lab provides comprehensive support, including technical assistance, training, and professional services, to help healthcare clients maximize their AI investments. For Life Sciences AI, they offer expertise in areas like drug discovery, clinical trials, and personalized medicine, helping organizations leverage AI for research and development while navigating complex scientific and regulatory landscapes.
Domino Data Lab's platform emphasizes reproducibility by tracking every aspect of the model development process, including code, data, environments, and results. This ensures that models can be re-run and validated at any time. For explainability, the platform supports tools and methodologies that help data scientists understand and interpret model predictions, which is vital for gaining trust in AI-driven clinical decisions and meeting regulatory requirements.
Domino Data Lab typically offers enterprise-grade pricing models that can be tailored to the specific needs and scale of an organization. While exact pricing details would require direct consultation, their models are designed to accommodate the growth of AI/ML initiatives within large healthcare systems, often based on factors like user count, computational resources, and platform usage.
Domino Data Lab has established itself as a leader in the Enterprise MLOps space, demonstrating strong company stability and growth. They have formed strategic partnerships with various technology providers and cloud platforms, which can be beneficial for healthcare organizations seeking integrated solutions. While specific healthcare-focused partnerships would need to be confirmed, their general partner ecosystem supports robust AI/ML deployments.

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