DataRobot intends to bolster the range of automated machine learning models that it can generate by bringing to market a time-series analytics add-on for its automated machine learning platform, which is designed to enable push-button predictive analytics in a few steps. Additionally, DataRobot is looking to expand its addressable audience to developers – the reason the company reached for Nexosis in July.
The 451 Take
DataRobot's new times-series analysis capabilities originally hail from the company's acquisition of Nutonian for an undisclosed sum in May 2017.
DataRobot continues to target organizations of a variety of sizes, including large enterprises, noting that it now has customers all over the world, including Europe, Japan
DataRobot uses a five-step process for push-button predictive analytics: ingest data, select target variables, build models (it typically builds 20 or more models behind the scenes to find the most appropriate), explore top models and get insight, and deploy the best model.
Having tested its new time-series modeling and analytics offering with more than 75 organizations for the past calendar year (DataRobot notes that it also has customers in production using it), the company has unleashed a product that is designed to provide the same user experience for time-series analysis as it has for other types of advanced analytics.
For those unfamiliar with this analytical technique, times-series analysis essentially involves the modeling and analysis of datasets in a sequence taken at successive points of time. It's a common and popular form of analytics used by companies for a variety of use cases, including sales planning, marketing planning
DataRobot rightly contends that time-series analysis is hard to successfully pull off. It requires a vast amount of data and variants, including historical datasets, and the ability to select the right
DataRobot Time Series includes advanced machine learning models for forecasting, as well as essential time-series methods like ARIMA and Facebook Prophet. Full API support helps AI engineers integrate modeling and prediction directly into business processes and applications.
DataRobot Time Series is designed to automate four areas of time-series analytics: feature engineering, target transformation, time-series modeling
Like DataRobot's existing platform, the company's time-series analysis add-on builds various model types behind the scenes in order to find the best one. It also includes visualizations specifically designed to graphically depict time-series
Although DataRobot has yet to divulge full roadmap details, integration between Nexosis and DataRobot's machine learning automation platform is planned. However, DataRobot also plans to continue selling Nexosis offerings stand-alone for the foreseeable future, while providing users with a pathway to DataRobot's automated machine learning platform.
Although DataRobot's acquisition of Nexosis was technology-driven, there is also an 'acquihire' aspect to it. DataRobot has retained all 16 Nexosis employees, including the startup's
Nexosis was founded in
Additionally, we think DataRobot will square up to fellow purveyors of enterprise data science platforms that also integrate with R and Python and deliver time series analytical modeling and forecasting – albeit not in quite the same manner as DataRobot. Dataiku, Anaconda
Big guns including SAS Institute in SAS Studio and Microsoft within SQL Server Analysis Services also address time-series modeling and analytics, albeit in a different manner to DataRobot.
When it comes to DataRobot's core automated machine learning platform, we continue to think Israeli startup DMWay Analytics is a DataRobot competitor because DMWay's platform also aims to automate machine-learning-based predictive analysis. AutoML, which is an open source project from the Machine Learning Lab at Freiburg University in Germany, also has a similar endgame.
Furthermore, H2O.ai has Driverless AI; RapidMiner peddles Auto Model, which we examine here; and Alteryx and BigML have also built capabilities of this ilk into their stacks. Additionally, Google is moving into this space with Cloud AutoML, which is currently in beta.
Indeed, we think it is fair to say that, while DataRobot pioneered the concept of automated machine learning in order to enable it to be used by all participants in a data science project, as well as in most applications, automated machine learning capabilities are becoming increasingly present in data science platforms.
As a Senior Analyst for the Data, AI & Analytics team, Krishna Roy is responsible for the coverage of self-service analytics, predictive analytics and performance management.
Jeremy Korn is a Research Associate at 451 Research. He graduated from Brown University with a BA in Biology and East Asian Studies and received
Aaron Sherrill is a Senior Analyst for 451 Research covering emerging trends, innovation