Description

Even if you have terabytes of business data, it may not be so easy to apply AI-based analytics on it. The bottleneck is often Machine Learning (ML) expertise and scalable infrastructure.

In this session, we'll start with how a data analyst can directly access vast amounts of data from the data warehouse directly in a spreadsheet. The data analyst can use tools such as charts and pivot tables to discover insights about their data. By connecting directly to the source with Connected Sheets, data integrity and security is preserved at all times.

Next, we'll look at how developers can build ML models in the cloud without deep ML expertise. Using SQL syntax, BigQuery ML enables developers to create robust models for regression, classification, time-series forecasting, and more. After the model is built, we'll see how an app developer could integrate the modeling code into the spreadsheet using JavaScript. This will enable the data analyst to train new models and predict right from their spreadsheet.

Finally, we'll look at an end-to-end scenario, solving a business problem with AI analytics. We'll see how a data scientist can go through the steps of training, evaluation, prediction, and even model retraining with BigQuery ML.

In this session, attendees from a variety of backgrounds, including data analysts, developers, data scientists, and managers, will see how to harvest insights from their business data in the cloud.


Local ODSC chapter in Austin, USA

Instructor's Bio

Karl Weinmeister

Cloud AI Advocacy Manager at Google

Karl Weinmeister is a Cloud AI Advocacy Manager at Google, where he leads a team of data science experts who develop content and engage with communities worldwide. Karl has worked extensively in machine learning and cloud technologies. He was a contributor to one of the first AI-based crossword puzzle solvers that is still referenced today.

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    AI-Based Analytics in the Cloud

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