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How Google BigQuery and Looker Can Accelerate Your Data Science Workflow

Most organizations have failed to achieve the value of predictive analytics. Typical data science workflows are resource intensive and the data environments within many companies are messy.

Data required for analysis can be strewn across so many tools and departments, that data scientists must spend most of their time (60%) preparing data for their analysis, rather than performing the analysis itself.

Join us for this webinar where Hossein Ahmadi (Google Cloud) and Marcell Babai (Looker Data Science Expert) will show you how you can leverage Looker with the power of BigQuery Machine Learning (BQML) to build machine learning (ML) models directly where your data lives.

You will learn how you can:

  • Simplify adoption and deployment of ML with BQML. Hossein will showcase real life customer use cases.
  • Reduce wait-time, operationalize workloads, and reduce complexity for quicker model development.
  • Leverage Looker to write the correct, performant SQL for you making it easy to handle hierarchical data.
  • Eliminate the need to move the data to another data science environment for certain types of predictive models.
Travix Case Study
Travix Case Study

Percolate Case Study
Percolate Case Study