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Machine Learning, Meet SQL: When ML Comes to the Database

Posted Apr 12
# Data engineering
# Systems and Architecture
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SPEAKER
Dan Sullivan
Dan Sullivan
Dan Sullivan
Principal Data Architect @ 4 Mile Analytics

Dan Sullivan is a Principal Data Architect at 4 Mile Analytics where he specializes in cloud and data architect with extensive experience in data architecture, data science, machine learning, stream processing, and cloud architecture. He is capable of starting with vague initiatives and formulating precise objectives, strategies, and implementation plans. He regularly works with C-level and VP executives while also mentoring and coaching software engineers. He is the author of the official Google Cloud study guides for the Professional Architect, Professional Data Engineer, and Associate Cloud Engineer.

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Dan Sullivan is a Principal Data Architect at 4 Mile Analytics where he specializes in cloud and data architect with extensive experience in data architecture, data science, machine learning, stream processing, and cloud architecture. He is capable of starting with vague initiatives and formulating precise objectives, strategies, and implementation plans. He regularly works with C-level and VP executives while also mentoring and coaching software engineers. He is the author of the official Google Cloud study guides for the Professional Architect, Professional Data Engineer, and Associate Cloud Engineer.

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SUMMARY

SQL has evolved beyond its relational origins to support non-relational abstractions like arrays, JSON, and geospatial data types so it shouldn’t surprise us that SQL is now being used to build and serve machine learning models. In this presentation, we’ll review how Google Cloud BigQuery supports regression, classification, forecasting, dimensionality reduction, and collaborative filtering. Feature processing, hyperparameter tuning, and evaluation functions are described as well. The talk concludes with a discussion of good practices for building and serving ML models in Google Cloud BigQuery.

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