DataRobot and the Quest for End-to-End Data Science

August 3, 2021

Boston-based DataRobot, a company which provides users with a data science and machine learning platform for building, deploying, and maintaining AI, is on a roll. Data science and machine learning is a process in which patterns in data are examined in order to make predictions and better understand the data at hand.

 At the end of July 2020, DataRobot announced two major milestones:

DataRobot was rated by G2 users as the Best at Meeting Requirements in the Summer 2021 G2 Grid® Report for Data Science and Machine Learning Platforms. If their users are satisfied with the offering (with a 94% rating, above the 89% category average), why did they acquire Algorithmia, which allows users to put models into production quickly, securely, and cost effectively? Although we do not have the inside scoop on their thought process, a likely reason can be that they are continuing to develop and hone their end-to-end data science solution.

Putting data science into production

DataRobot has been on a mission to provide an end-to-end solution for data science, from data preparation to data insights. Aside from internal product development, they have fueled this quest through their seven acquisitions since 2017, as can be seen in the chart below.

DataRobot's data science and machine learning acquisitions

DataRobot acquisitions fuel end-to-end data science

MLOps category on G2 is on fire

One thing is for sure: the AI & Machine Learning Operationalization software (MLOps) category on G2, in which Algorithmia finds itself, is on fire. As can be seen in the chart below, the category saw a major spike in traffic in July 2021, up 10 times compared to the previous month and the beginning of the year.

traffic to AI and machine learning operationalization software

The need for this software, which allows users to manage and monitor machine learning models as they are integrated into business applications, is clear and concrete. Without it, businesses can monitor and maintain models but may struggle with integrating and deploying these models across the business.

As Algorithmia noted in their 2021 enterprise trends in machine learning report, AI and machine learning initiatives are top priorities for many organizations. However, without proper MLOps tools and strategies in place businesses will end up spending more time, energy, and resources on model deployment.

With Algorithmia in their pocket and $300 million in the bank, DataRobot will be able to add more tools in their toolbox for model deployment, thus improving on their end-to-end solution. This, in turn, will help businesses succeed in transforming their data into insights and driving business value.

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DataRobot and the Quest for End-to-End Data Science With their latest acquisition and $300 million fundraise, DataRobot is hedging their bets on the commercialization of algorithms and moving lab to live.
Matthew Miller Matthew Miller is a research and data enthusiast with a knack for understanding and conveying market trends effectively. With experience in journalism, education, and AI, he has honed his skills in various industries. Currently a Senior Research Analyst at G2, Matthew focuses on AI, automation, and analytics, providing insights and conducting research for vendors in these fields. He has a strong background in linguistics, having worked as a Hebrew and Yiddish Translator and an Expert Hebrew Linguist, and has co-founded VAICE, a non-profit voice tech consultancy firm.