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The new approach to AI: Why businesses rush to deploy machine learning

Until now, handling time-dependency modelling required highly specialised expertise to even set up the problem correctly. 

Conventional modelling uses randomly selected records from a dataset to build and evaluate predictive models. 

For example, a representative sample of loan records and whether a customer defaulted. Each record is relatively similar.

To address this issue DataRobot announced the general availability of DataRobot Time Series.
 
Building on the 2017 acquisition of Nutonian and its proven Eureqa modelling engine, DataRobot Time Series supposedly understands all these questions and how to set up the problem based on the answers. 

Then the DataRobot automation platform supposedly constructs and evaluates hundreds to thousands of different time series models and scores their performance – taking into account all the different temporal conditions to determine real-world accuracy.

DataRobot Time Series beta customers, including Fortune 2000 retailers, banks, and hospital networks, have quickly built accurate models for staffing, inventory management, demand forecasting, financial applications, and more – all without the need for manual forecasting, specialized data science expertise, and custom coding.
 
DataRobot chief scientist Michael Schmidt says, “Forecasting underpins most critical business functions. If you can predict the future, you can usually win the game. 

“But it is one of the hardest problems in data science. Since the Nutonian acquisition last May, we’ve been on a massive undertaking to combine Nutonian and DataRobot innovations into the best time series product in the world.” 

“This fourth version, which has been extensively tested by customers in production, automates a wide array of advanced best practices in areas like feature engineering and thereby achieves a whole new level of accuracy.”

This new version, which is available now, 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 modelling and prediction directly into business processes and applications.

DataRobot offers an enterprise machine learning platform that supposedly empowers users of all skill levels to develop and deploy machine learning and AI faster. 

Incorporating a library of hundreds of the most powerful open source machine learning algorithms, the DataRobot platform automates, trains, and evaluates models in parallel, delivering AI applications at scale.