As of May 2026, there are 3,358 SaaS companies in Machine Learning Software. They have combined revenues of $46.8B and employ 425.9K people. They have raised $59.3B and serve 2.9B customers combined.
Machine Learning Software encompasses tools and applications that enable systems to learn from data and improve their performance over time without explicit programming. These solutions often utilize algorithms and statistical models to analyze patterns, make predictions, and automate decision-making processes across various domains. Common use cases include predictive analytics, natural language processing, image recognition, and anomaly detection. Commonly adopted in sectors such as finance, healthcare, marketing, and IT, machine learning software is primarily used by data scientists, business analysts, and IT professionals. Typical features include data preprocessing, model training, evaluation, and deployment, which facilitate the integration of machine learning capabilities into existing workflows. By leveraging large datasets, organizations can enhance operational efficiency, improve customer experiences, and make better-informed strategic decisions.
Sorting: Highest -> Lowest
Showing 10 of 422 companies ranked by annual revenue.
New York, New York, United States
retrain.ai which uses AI and machine learning to read job boards at scale and gain insight into where the job market is going.
New York, New York, United States
Developer and provider of data science platform intended for data scientists. The company's SaaS platform which equips data science teams with high-leverage automation tools which include one-click publishing and sharing, scaling to GPUs, flexible custom environments, and eliminating hours of traditional, manual work, enabling companies to perform data science at a new level of scale, with one-click solutions.
Tulsa, Oklahoma, United States
Developer of a cloud-based emotional analytics platform created to measure and understand emotions in real-time. The company's platform combines neuroscience, cognitive technology and machine learning to understand emotions and their impact on learning, health and well-being as well as tracks, predicts and recommends customers based on relevant emotional states, enabling users to receive a new technology of artificial intelligence with empathy to improve their lives.
Nuremberg, , Germany
Innovation OS - one platform for everything innovation The ITONICS Innovation OS combines human ingenuity and machine intelligence in a collaborative innovation management software.
Luxembourg
Next Gate Tech, a Luxembourg-based FundTech, provides SaaS solutions for asset management with machine learning, data analytics, and cloud-based automation.
New York, New York, United States
Entera is the leading 3-sided marketplace focused on connecting Enterprise and Mid-Market investors, sellers and services providers in the single-family residential market. Powered by modern data science and Artificial Intelligence (AI), Entera’s marketplace offers the most seamless solution for investors to access, evaluate, transact and operate their single-family real estate investments. Backed by leading venture capital investors Goldman Sachs Growth, Bullpen Capital, and Craft Ventures, Entera is focused on delivering software innovation and offline service solutions that enable its’ investor clients to scale their operations quickly, make more market data-driven decisions confidently, and maximize financial returns. Since its inception in 2018, Entera has transacted on more than 15,000 single family homes valued at $5 Billion across 29 US markets. The company is headquartered in New York City, New York and Houston, Texas. Learn more at www.entera.ai.
- Must provide tools for data preprocessing and model training - Should support both supervised and unsupervised learning methods - Must enable the deployment of trained models for real-world application - Should include analytics capabilities for model evaluation and performance tracking - Must cater to users such as data scientists, analysts, and software engineers - Not just for simple data visualization; must also enable predictive modeling and automation
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