Story image

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.

Red Hat expands integration product capabilities
Adds end-to-end API lifecycle support and new capabilities for agile integration across hybrid architectures.
Oracle updates enterprise blockchain platform
Oracle’s enterprise blockchain has been updated to include more capabilities to enhance development, integration, and deployment of customers’ new blockchain applications.
BMC adds IBM Cloud, Watson to Helix solution
BMC Helix with IBM Watson delivers cognitive insights across structured and unstructured federated knowledgebases.
Hyundai works with IBM to create a new blockchain-based platform
The network for commercial financing will supposedly provide participants with a single view of all the transactions happening in the network.
Why businesses should invest in energy automation
In industrial applications digital transformation allows businesses to do more with less.
NZ Cricket ups data analytics game with Qrious
The Black Caps and White Ferns have implemented a data and analytics solution from Qrious to monitor and improve game strategy and player performance.
Gartner: Smartphone biometrics coming to the workplace
Gartner predicts increased adoption of mobile-centric biometric authentication and SaaS-delivered IAM.
IDC: A/NZ second highest APAC IoT spenders per capita
New IDC forecast expects the Internet of Things spending in Asia/Pacific excluding Japan to reach US$381.8 Billion by 2022.