Domino offers a machine learning operations (MLOps) platform for enterprises. The platform helps data scientists to build, deploy, and monitor artificial intelligence (AI) applications using preferred tools and languages. It serves financial services, healthcare, insurance, and more industries. The platform was founded in 2013 and is based in San Francisco, California.
Domino's Product Videos
ESPs containing Domino
The ESP matrix leverages data and analyst insight to identify and rank leading companies in a given technology landscape.
The AI development platforms market offers a range of solutions to help organizations build and integrate AI-powered solutions to deliver on commercial objectives. These platforms address common obstacles faced by organizations, such as siloed and messy data, slow decision-making despite having more data available, and difficulty in productionizing AI projects. The platforms also offer capabilitie…
Research containing Domino
Get data-driven expert analysis from the CB Insights Intelligence Unit.
CB Insights Intelligence Analysts have mentioned Domino in 3 CB Insights research briefs, most recently on Apr 14, 2023.
Expert Collections containing Domino
Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.
Domino is included in 4 Expert Collections, including Tech IPO Pipeline.
Tech IPO Pipeline
Future Unicorns 2019
Companies developing artificial intelligence solutions, including cross-industry applications, industry-specific products, and AI infrastructure solutions.
104 insurtech companies addressing 10 technology priorities, from catastrophe modeling to fraud analytics, that P&C insurers face.
Latest Domino News
Aug 11, 2023
3 mins read Unlocking the Future! A comprehensive look at the top 10 no-code data science tools for 2023 In an era where data drives decisions, the realm of data science continues to evolve, and with it, the tools that facilitate its exploration. Introducing the “Top 10 No-Code Data Science Tools for 2023” – a comprehensive guide to the cutting-edge solutions reshaping data analysis. These tools transcend the barriers of complex coding, making data science accessible to all. From uncovering insights to automating processes, they empower users to harness the potential of their data without the need for extensive technical expertise. We delve into each tool’s unique offerings, revolutionizing how we navigate and extract value from the world of data. Teachable Machine by Google: This tool allows you to create machine learning models without coding knowledge. You can use it to build image classifiers, object detectors, and other models. With its user-friendly interface, Teachable Machine democratizes machine learning, enabling individuals from various backgrounds to harness its power effortlessly. Orange Data Mining: This open-source tool provides a visual interface for data mining and machine learning tasks. It is easy to use and has many features, making it a good choice for beginners and experienced data scientists. Orange Data Mining allows users to intuitively explore complex data relationships, fostering a deeper understanding of data-driven insights. Obviously.ai: This tool helps you build machine learning models by answering simple questions. It is ideal for people with no coding experience who want to get started with machine learning quickly. Obviously.ai streamlines the entry into the world of machine learning, making it accessible and engaging for newcomers to the field. RapidMiner: This commercial tool offers a wide range of data preparation, machine learning, and model deployment features. It is a good choice for businesses and organizations that need to build and deploy machine learning models at scale. RapidMiner accelerates the deployment of machine learning solutions, catering to enterprises seeking efficient and robust model implementation. H2O.ai: This open-source tool provides a unified platform for data science. It includes features for data preparation, machine learning, and model deployment. H2O.ai is a good choice for data scientists who want a powerful and flexible platform for their work. With its versatile toolkit, H2O.ai enables data scientists to execute end-to-end workflows seamlessly. DataRobot: This commercial tool automates the machine learning process. It can build and deploy machine learning models without any human intervention. DataRobot is a good choice for businesses that need to build machine learning models quickly and easily. DataRobot revolutionizes model development, optimizing efficiency by automating intricate tasks. Trifacta: This tool helps you clean and prepare your data for machine learning. It has a visual interface that makes it easy to identify and fix data problems. Trifacta is a good choice for businesses that must ensure their data is clean and ready for machine learning. Trifacta empowers organizations to enhance data quality, a critical step towards reliable machine learning outcomes. Domino Data Lab: This commercial tool provides a collaborative workspace for data science teams. It includes features for data preparation, machine learning, and model deployment. Domino Data Lab is a good choice for businesses managing and collaborating on data science projects. Domino Data Lab fosters teamwork and innovation among data science professionals with its collaborative environment. Tableau: This commercial tool is used for data visualization. It can create interactive dashboards and reports that help you understand your data. Tableau is a good choice for businesses that want to visualize their data to make better decisions. Tableau transforms raw data into meaningful insights, enabling informed decision-making through impactful visualizations. Amazon SageMaker: This cloud-based service provides a fully managed machine learning platform. It includes features for data preparation, machine learning, and model deployment. Amazon SageMaker is a good choice for businesses that want to build and deploy machine learning models on the cloud. Amazon SageMaker empowers companies to harness the scalability and flexibility of cloud computing for their machine learning endeavors. Disclaimer: Any financial and crypto market information given on Analytics Insight are sponsored articles, written for informational purpose only and is not an investment advice. The readers are further advised that Crypto products and NFTs are unregulated and can be highly risky. 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Domino Frequently Asked Questions (FAQ)
When was Domino founded?
Domino was founded in 2013.
Where is Domino's headquarters?
Domino's headquarters is located at 135 Townsend Street, Floor 5, San Francisco.
What is Domino's latest funding round?
Domino's latest funding round is Series F - II.
How much did Domino raise?
Domino raised a total of $223.6M.
Who are the investors of Domino?
Investors of Domino include Snowflake Ventures, Sequoia Capital, Coatue Management, Highland Capital Partners, NVIDIA and 10 more.
Who are Domino's competitors?
Competitors of Domino include Databricks, Fiddler AI, Shakudo, DataRobot, Dataiku and 16 more.
Compare Domino to Competitors
DataRobot operates as an artificial intelligence (AI) lifecycle platform with ecosystem integrations. It offers solutions such as augmented intelligence, data engineering, machine learning, artificial intelligence, and more and serves the banking, healthcare, retail, manufacturing, and public sectors. The company was founded in 2012 and is based in Boston, Massachusetts.
Seldon Technologies builds global infrastructure for machine learning (ML) operations. It accelerates the adoption of machine learning to improve business performance and manage risk. It enables data scientists to speed up the process of data interpretation. It was founded in 2014 and is based in London, United Kingdom.
Databricks provides a unified analytics platform. It offers a collaborative solution for the machine learning lifecycle such as featurization production and also provides data analytic services to simplify data integration. It serves financial services, healthcare and life sciences, manufacturing, communications, media and entertainment, and public sector. Databricks was founded in 2013 and is based in San Francisco, California.
Dataiku develops a centralized data platform to help businesses in their data journey from analytics at scale to enterprise artificial intelligence (AI). Its solutions include data preparation, visualization, machine learning, analytic applications, and more. The company serves the banking sector, pharmaceuticals, manufacturing telecommunication sector, and more. It was founded in 2013 and is based in New York, New York.
Tecton offers a feature platform for machine learning (ML). The platform helps businesses to build and manage machine learning models. It offers machine learning solutions, fraud detection, search and ranking, financial market forecasting, and more. The company was founded in 2019 and is based in San Francisco, California.
Modzy provides a secure ModelOps AI enterprise platform to discover, deploy, manage and govern AI at scale. The platform offers a rapid deploy-anywhere approach and a model marketplace with vetted and pre-trained models from AI companies. Modzy includes explainability to increase AI trust levels and adversarial defense for resilience against data poisoning and other threats.