Deep Dive on Time Series Forecasting and Anomaly Detection With BigQuery ML

Polong Lin & Stephanie Wang

Developer Advocate, BigQuery ML and Senior Developer Relations Engineer at Google

About Polong and Stephanie

Stephanie is a Senior Developer Relations Engineer at Google Cloud Platform. Stephanie primarily focuses on building and maintaining the BigQuery Java Client Libraries which help developers interact with BigQuery APIs more easily. Prior to Google, Stephanie worked as a software engineer at Morgan Stanley and led a team of 10 to build sales and trading applications. Outside work, Stephanie is passionate about animals — she has an insta-famous dog, @sasha_the_shibapoo and a opinionated semi-aquatic turtle, @bombshell_the_turtle.

Polong is a Senior Developer Advocate for Google Cloud, with a focus on the intersection of big data, data science and machine learning. He has taught data science to hundreds of thousands of learners online. Follow him on Twitter @polonglin.

Talk Description

Time series forecasting is a powerful way to use current or historical data to monitor, clarify, and predict “cause and effect” behaviors which can better inform future decisions. Anomaly detection for a time series can detect critical incidents such as fraudulent behaviors or potential opportunities such as changes in consumer buying patterns. Join this talk, to see the newest features in BigQuery ML in action and simplify your workflow in time forecasting and anomaly detection.

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