DataRobot, the Boston-based Data Science company, enables business analysts to build predictive analytics with no knowledge of Machine Learning or programming. It uses automated ML to build and deploy accurate predictive models in a short span of time.
DataRobot was founded in 2012 in Boston by Jeremy Achin and Tom de Godoy. Both of them come with extensive experience in dealing with data science and ML models. In the most recent funding round, it has raised $54 million in Series C by New Enterprise Associates (NEA). The company has so far raised about $125 million from Accomplice, NEA, IA Ventures and Intel among other investors.
Source: DataRobotDataRobot
AutoML is revolutionizing data and AI domains by bringing the power of predictive analytics to businesses. An analyst who is familiar with mainstream business intelligence tools can leverage AutoML platforms to build and deploy highly sophisticated machine learning models. Experienced data scientists can go multiple levels deeper than a business analyst to customize and optimize the models.
The DataRobot platform has evolved during the last few years to take advantage of the innovations in the public cloud. Enterprises can choose to run the software either in the public cloud or on-premises data center. The hosted version dubbed as DataRobot Cloud Platform currently runs on AWS. At AWS re:Invent last year, the company achieved Amazon Web Services (AWS) ML Competency status. DataRobot claims that customers have built over 500,000,000 models on the DataRobot Cloud on AWS.
DataRobot delivers a wizard-style of user experience to generate Machine Learning models. In just six steps, businesses can deploy a real-time predictive analytics service backed by an accurate Machine Learning model.
It all starts by uploading the dataset to DataRobot platform, which accepts input from a file, a remote URL, a JDBC data source or HDFS.
Once data is ingested, the platform infers the schema by suggesting appropriate data types for each feature. Business analysts and data scientists can perform necessary to advanced data exploratory activities on the ingested dataset. Finally, they need to select the target label which is going be predicted by the model.