Nearly every business has access to data that can be used to its advantage if harnessed correctly. Data fuels machine learning models, and with the right tools, companies can process this data and build models that will help them compete in a rapidly changing market, respond more quickly to changing customer requirements, and find insights faster.
DataRobot covers the entire machine learning lifecycle, such as preparing data, operationalising it, and building APIs to make it worthwhile for the organisation to create a complete solution. Its broad platform approach has appealed to investors.
The company has attracted investors by offering a machine learning platform that helps analysts, developers, and data scientists build predictive models faster than traditional methods. Once created, the company provides a way to deliver the model as an API, simplifying deployment.
The company has always aimed to simplify machine learning across industries so that even a business analyst with a basic understanding can run predictive models. In a funding round led by Altimeter Capital and Tiger Global, the US-based startup raised $300 million to expand the business.
Besides strengthening its platform, the fresh infusion of funds also expands across North America, EMEA and APAC. The billion-dollar valuation made the company the highest-ranking of the “picks-and-shovels” startups featured on Forbes’ inaugural AI 50 list — the companies that provide tools to help their customers develop their own AI.
Machine learning, which identifies patterns and predicts future events without explicit programming, is becoming popular among companies of all sizes.
DataRobot attempts to automate as much of the traditional job of a data scientist as possible. Customers provide data and a business question, and the DataRobot system turns out accurate models for a given task. For instance, Lenovo used DataRobot to estimate detailed demand in Brazil. United Airlines wanted to predict which passengers might gate-check their bags with the help of the company. Similarly, the Philadelphia 76ers improved their season-ticket renewal process by using their system.
“You don’t need all these different personas — data engineers, data scientists, application developers — a business analyst can do the whole thing themselves,” said Igor Taber, strategy executive at DataRobot. “It abstracts the underlying complexity, so we can shrink the time to production and see value from what could be years into weeks.”
As an investor at Intel Capital, Taber discovered DataRobot and participated in the Series B round. Its explosive growth and founder and (former) CEO Jeremy Achin’s leadership convinced him to join the company full-time at the beginning of 2019.
Achin and co-founder Tom de Godoy have been running the company since 2012 when they quit their research and data modelling jobs at Travelers Insurance because they believed that the supply of data scientists would not meet the demands of the coming decades.
The company has hundreds of enterprise customers in BFSI, healthcare, sports, retail, marketing, and agriculture. Its platform has been used to build more than 1.3 billion models. In addition to continuing software development, the company is planning to use upcoming funds to target acquisitions. Meanwhile, it acquired three smaller machine-learning startups, including ParallelM, in June, leading to a new product that monitors companies’ models for biases or inconsistencies.
“We are automating more and more of the process,” said Achin. “We have so many ideas for different markets and different products — it feels like there’s an infinite road map ahead.” Despite the funding so far, Achin says that another round of funding wouldn’t be out of the question as more competitors launch their automated machine-learning products. “We were early, but others have started chasing us,” he says. “There’s still an opportunity to own the AI market, and I think we’re always going to be hungry for more capital.” Since the pandemic has brought more business online, companies are always looking for an edge, and AI and machine learning are great ways to do so.
DataRobot’s mission is to change how companies all over the world make their most important decisions. They realise their vision by delivering powerful AI and machine learning solutions relevant and accessible to everyone.
The DataRobot enterprise AI platform democratises data science. It is available for on-premises, cloud-based, or managed AI service deployment using the latest open-source algorithms. One of the essential elements of a successful enterprise AI platform is empowering anyone to build and maintain AI applications quickly and easily. DataRobot claims to automate all ten steps needed to develop and deploy advanced AI applications. Its enterprise AI platform includes four independent but fully integrated products, each of which can be implemented in various ways based on your business needs and IT requirements.
Enterprise AI in Finech
Fintech firms are looking to leverage AI and predictive modelling in all areas of the industry — be it payments, investing, lending, digital wealth, personal finance, capital markets or one of the many others — to increase revenues, grow their customer base, improve efficiency and manage risk. It provides automation and ease of use for enterprise AI initiatives. From AI in banking to AI in asset management and AI in credit risk — it allows fintech companies to build and deploy enterprise AI models quickly and with ease.
AWS + DataRobot: Technology Alliance
On AWS, the DataRobot cloud provides organisations of all sizes to get up and running with automated machine learning. DataRobot cloud on AWS reduces overhead and allows organisations to scale machine learning services up and down as needed, only paying for the resources they need. The solution is delivered through a software-as-a-service (SaaS) delivery model by DataRobot, an AWS Partner Network (APN) Partner with machine learning and financial services competencies. DataRobot cloud on AWS help organisations efficiently answer complex business questions and make accurate predictions at scale without hiring data scientists to develop and interpret machine learning models.
DataRobot in the news
DataRobot has acquired BCG’s Source AI technology. This relationship combines proprietary IP with leading consulting services, thus providing both the human expertise and the technical know-how needed to deliver optimal, continuous value from AI. As organisations worldwide increasingly deploy AI to steer decision-making and bolster business performance, many struggles to deploy and manage models in production and derive value. A survey conducted by BCG and MIT Sloan Management Review found that seven out of ten companies report minimal or no impact from AI deployments. To address this, AI platforms that allow organisations to build, deploy, monitor, and manage machine-learning models are proving critical, and DataRobot’s acquisition of BCG’s Source AI technology will help clients capitalise on AI.
The DataRobot leadership team comprises visionaries, data scientists, and seasoned veterans with experience building world-class companies that drive value and change the face of technology.
Dan Wright serves as CEO, the leader in Augmented Intelligence. As CEO, Wright drives the company’s strategic direction to democratise AI, enabling organisations across the globe to solve their most pressing challenges with AI. Michael Schmidt serves as CTO, where he is responsible for pioneering the next frontier of Augmented Intelligence and the company’s future cutting-edge technology. Nick King serves as CMO, where he drives the company’s brand and marketing strategy through trusted and impact-driven storytelling and leads alliances and strategic partnerships.
DataRobot has gained leadership through its inventions, innovations and acquisitions. Here is a list of DataRobot’s many mergers and acquisitions.
In 2017, It acquired Nutonian to bolster the automated machine learning platform with time series modelling capabilities.
In 2018, It acquired Nexosis to bolster the company’s lead in enabling the AI-driven enterprise.
In 2019, It acquired Cursor, ParallelM and Paxata to bolster the company’s data management capabilities, making data and AI even more accessible and creating an industry-leading MLOps and governance.
In 2021, It acquired Zepl and Algorithmia to enable data scientists to code their tasks and custom models. Also, the company partnered with Hexaware to empower businesses across industries to accelerate their AI initiatives.
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