Looker Enhances Data Science Capability With Integration for Google BigQuery ML

Looker Enhances Data Science Capability With Integration for Google BigQuery ML

Removes Machine Learning Bottlenecks and Empowers Business Users with Self-Serve Predictive Metrics

Looker, a leading data platform company, announced an integration with Google Cloud BigQuery ML (BQML) that accelerates the time-to-value of data science workflows and allows business users to operationalize insights with interactive predictive metrics.

@lookerdata builds integration for Google BigQuery ML, optimizing data science workflows. @googlecloud #datascience #machinelearning #GoogleNext18

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.

“Much of the work in machine learning centers around data preparation and ML model evaluation and tuning,” said Lloyd Tabb, Looker Co-founder, Chairman and CTO. “Looker and BigQuery ML are great together in that Looker handles the data preparation and BigQuery ML does the learning. 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.”

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“BigQuery ML brings machine learning closer to where customers are storing large datasets, so they can quickly create and deploy models, at scale,” said Sudhir Hasbe, Director of Product Management, Google Cloud. “Looker’s integration with BigQuery ML adds powerful capabilities for our joint customers who can now use Looker to run ML models directly in BigQuery and surface the predictive insights across their organizations.”

“Looker and BigQuery have allowed us to arm our content creators, producers and every department at BuzzFeed with the data and insights they need to make decisions and iterate rapidly,” said Nick Hardy, Data Scientist at BuzzFeed. “With the introduction of BigQuery ML, we can further expand the ways these products are impacting our Data Science workflow — we’re excited to see what new opportunities it unlocks.”

Looker Accelerates the Data Science Workflow

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.

Recommended Read: Figure Eight Enters Into New Collaboration with Google Cloud

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