Drawing Insights with BigQuery ML

Developed with
Calculated Systems

Lab Steps

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Signing In to the Google Cloud Console
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Opening the Lab's Jupyter Notebook in Google Cloud

Ready for the real environment experience?

DifficultyIntermediate
Time Limit1h
Students25
Ratings
5/5
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Description

Machine learning is proving to be very powerful in gathering insights with your data. BigQuery ML allows the user to perform Machine Learning training, evaluation, and prediction on large sets of data. This lab will explain some of the basic concepts along with an example of training a linear regression and binary logistic regression model.

Learning Objectives

Upon completion of this lab you will be able to:

  • Utilize BigQuery ML to train, evaluate and predict with a linear regression model
  • Utilize BigQuery ML to train, evaluate and predict with a binary logistic regression model
  • Visualize a BigQuery ML Dataset

Intended Audience

This lab is intended for:

  • Data engineers
  • Anyone interested in gaining insights from BigQuery ML

Prerequisites

You should possess:

  • A basic understanding of BigQuery ML
  • A basic understanding of data engineering concepts
About the Author
Students1529
Labs14
Courses7
Learning paths11

Calculated Systems was founded by experts in Hadoop, Google Cloud and AWS. Calculated Systems enables code-free capture, mapping and transformation of data in the cloud based on Apache NiFi, an open source project originally developed within the NSA. Calculated Systems accelerates time to market for new innovations while maintaining data integrity.  With cloud automation tools, deep industry expertise, and experience productionalizing workloads development cycles are cut down to a fraction of their normal time. The ability to quickly develop large scale data ingestion and processing  decreases the risk companies face in long development cycles. Calculated Systems is one of the industry leaders in Big Data transformation and education of these complex technologies.