ai datarobot 250m altimeter capital tiger

ai datarobot 250m altimeter capital tiger

The Power of Automated Machine Learning

Automated machine learning (AutoML) has emerged as a game-changer in the field of data science. Traditional machine learning models require extensive manual intervention and expertise to build, test, and deploy. However, with AutoML platforms like DataRobot, organizations can automate the entire machine learning pipeline, from data preparation to model deployment. This significantly reduces the time and effort required to develop accurate and robust predictive models.

DataRobot’s platform utilizes advanced algorithms and AI techniques to automate the process of feature engineering, model selection, hyperparameter tuning, and model evaluation. By automating these complex tasks, DataRobot enables data scientists and business analysts to focus on higher-value activities such as interpreting results, making strategic decisions, and driving business outcomes.

Investment Highlights

The recent funding round led by Altimeter Capital and Tiger Global highlights the confidence investors have in DataRobot’s technology and its potential for growth. The $250 million investment will be used to further enhance DataRobot’s platform capabilities, expand its product offerings, and accelerate its global expansion plans.

DataRobot has already established itself as a leader in the AutoML space, with a customer base that includes Fortune 500 companies, government agencies, and leading academic institutions. The company’s platform has been widely recognized for its ability to democratize AI and make it accessible to a broader audience, including business users with limited coding or data science expertise.

Market Potential and Competitive Landscape

The AI and machine learning market is experiencing rapid growth, with organizations across industries recognizing the transformative potential of these technologies. According to a report by Grand View Research, the global AI market size is expected to reach $733.7 billion by 2027, growing at a CAGR of 42.2% from 2020 to 2027. This presents a significant opportunity for DataRobot to capitalize on the increasing demand for automated machine learning solutions.

While DataRobot has established itself as a leader in the AutoML space, it faces competition from other players such as Google’s AutoML,, and Databricks. However, DataRobot’s comprehensive platform, ease of use, and strong customer base give it a competitive edge in the market. The recent funding round will enable DataRobot to further innovate and differentiate itself from competitors, solidifying its position as a market leader.

The Future of AI DataRobot

With the new capital infusion, DataRobot is well-positioned to accelerate its growth and expand its global footprint. The company plans to invest in research and development to enhance its platform capabilities and bring new innovations to market. Additionally, DataRobot aims to strengthen its customer success initiatives, ensuring that organizations can derive maximum value from its platform.

Furthermore, DataRobot intends to leverage the funding to fuel its international expansion efforts. The company already has a presence in major markets such as the United States, Europe, and Asia-Pacific. By expanding its operations in these regions and entering new markets, DataRobot can tap into the growing demand for AI and machine learning solutions worldwide.


The $250 million funding round led by Altimeter Capital and Tiger Global is a testament to the growing importance of AI and machine learning in today’s data-driven world. DataRobot’s automated machine learning platform has already made significant strides in democratizing AI and empowering organizations to harness the power of data. With this new capital infusion, DataRobot is poised to further enhance its platform capabilities, expand its market reach, and solidify its position as a leader in the AutoML space. As the demand for AI continues to grow, DataRobot is well-positioned to drive innovation and shape the future of automated machine learning.

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