
San Francisco, California, United States
a cloud-based data engineering, data science, and machine learning platform
- Revenue
- $3.7B
- Customers
- 10K
- Year founded
- 2013
- Funding
- $4B
- Team size
- 12.3K
- Growth
- 54.17%
As of May 2026, there are 4,321 SaaS companies in Data Science and Machine Learning Platforms. They have combined revenues of $52B and employ 466.7K people. They have raised $55.6B and serve 3.8B customers combined.
Data Science and Machine Learning Platforms are software solutions designed to facilitate the analysis, modeling, and interpretation of complex data sets using statistical and machine learning techniques. These platforms enable data scientists to build, train, and deploy machine learning models efficiently, often featuring tools for data visualization, preprocessing, and collaboration among team members. They support various workflows, including data ingestions, model development, and performance monitoring, catering to organizations aiming to leverage data-driven insights for decision-making. Typical use cases for these platforms include predictive analytics, customer segmentation, and operational optimization. They are utilized across various industries such as finance, healthcare, marketing, and technology. Common buyer personas include data scientists, data analysts, business intelligence professionals, and IT managers, who seek to extract actionable insights from data and improve business processes through advanced analytics and machine learning capabilities.
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Showing 10 of 90 companies ranked by annual revenue.

San Francisco, California, United States
a cloud-based data engineering, data science, and machine learning platform

San Francisco, California, United States
AI-powered technology enabler and digital transformer of American…

San Francisco, California, United States
The world's most powerful data labeling and RLHF platform, designed for the next generation of AI

Seoul, South Korea
MegazoneCloud accelerates your journey to the AI era with comprehensive cloud, AI, and cybersecurity solutions.

Munich, Bayern, Germany
process mining platform to help businesses analyze, visualize, and optimize their processes.

Singapore
Advance Intelligence Group is an AI-driven technology company specializing in financial technology and services, founded in 2016 in Singapore. They provide AI-powered credit-enabled products and services in various sectors including financial services and retail.

Montreal, Quebec, Canada
Hopper is a mobile app that leverages big data and machine learning to predict and analyze airfare

Toronto, Ontario, Canada
Tenstorrent is a next-generation computing company that builds computers for AI.

San Francisco, California, United States
We are a tech-enabled service providing solutions to the world’s most complex business problems. Driven by our proprietary process orchestration platform, we seamlessly integrate advanced AI and automation with a global network of over 3,500 experts. This powerful combination delivers new capabilities and eliminates barriers to execution for our clients, unlocking unprecedented efficiency, scale, and growth opportunities.

Danvers, Massachusetts, United States
DemandScience is the premier AI-powered, B2B demand generation company accelerating global growth for our clients. The DemandScience intelligence platform empowers B2B organizations to swiftly identify the right accounts and target in-market buyers with precision. By combining groundbreaking technologies and AI innovation, the company ensures timely delivery of accurate data, intelligence, and insights, adding value to the end-to-end journey from initial engagement to conversion. Founded in 2012, DemandScience provides 1,500 global customers with superior marketing solutions, B2B data, and leads.
- Must provide comprehensive tools for data preparation, analysis, and model deployment. - Must support collaboration features for data scientists and business stakeholders. - Must include capabilities for both supervised and unsupervised machine learning. - Must allow for the integration of various data sources and formats. - Not just for analysis; must also provide tools for model training and evaluation.
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