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1
Intro
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Welcome
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Recording
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Boulder Denver Group
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Databricks Summit 2022
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Fan and Eric
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Introduction
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Agenda
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TMobile Marketing Solutions
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Magenta Marketing Platform
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Why dont we just use this data directly
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How are demographic insights used
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Pandas
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UDF
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Improving XGBoost
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Data set
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Why XGBoost
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What we did
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How did we achieve that
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Parallelizations
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Autoscaling
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Normal transformation
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Pivot vs Vector
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RDD
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Questions
Description:
Explore scaling techniques for XGBoost models with thousands of features in this 51-minute conference talk from Databricks. Dive into an online advertising use case that enables marketers to target users based on demographic information. Learn about the challenges faced, mistakes made, and valuable insights gained during the process of scaling XGBoost model training. Discover common pitfalls to avoid and notable differences between Python and Scala implementations of XGBoost in Spark. Gain practical knowledge from experts Phan Chuong and Eric Yatskowitz as they share their experiences in scaling machine learning models for production environments and supporting marketing decisions with data insights.

Scaling XGBoost for Thousands of Features with Databricks

Databricks
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