Looker is integrating its platform with Google Cloud BigQuery ML (BQML), accelerating the time-to-value of data science workflows.
With Looker and BigQuery ML, data teams can now save time and eliminate unnecessary processes by creating machine learning (ML) models directly in BigQuery via Looker – without the need to transfer data into additional ML tools. BigQuery ML predictive functionality will also be integrated into new or existing Looker Blocks allowing users to surface predictive measures in dashboards and applications.
“Looker and BigQuery ML are great together in that Looker handles the data preparation and BigQuery ML does the learning,” said Lloyd Tabb, Looker Co-founder, Chairman and CTO. “Looker can also help you evaluate and tune ML models to integrate predictions into dashboards and data workflows. We look forward to continuing our work with Google and bringing BigQuery ML capability to Looker Blocks."
Looker provides a single, governed lens into an entire organization’s data. It accelerates the data science stack by removing the struggle to prepare data and freeing up time for data scientists to leverage ML at scale and use their unique skill set to perform higher-value tasks.
Unified and cleaned data also delivers efficiency and clarity by quickly and accurately surfacing business insights for better context.
Businesses can now move from data to decisions faster by leveraging leading analytic technologies to operationalize the outputs of ML models and take action instantly.
For more information about this news, visit www.looker.com.