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西格玛计算:如何在 Sigma 中使用输入表改进 Databricks 中的数据科学和机器学习应用程序.pdf

上传人: 2*** 编号:139035 2023-06-04 9页 1.29MB

1、Sigma+DatabricksHow using Sigma Input Tables improves Data Science and Machine Learning within Databricks Databricks2023Introduction:SpeakersMitch is an experienced data analytics professional with a 12-year tenure in both managerial and individual contributor roles.Before joining Sigma,Mitch spent

2、the 3 prior years running data teams on Databricks.Mitch ErtlePartner Solutions Engineer,DGreg is a software engineer,chocolatier,and former synthetic biologist with a background in distributed systems.Greg leads the input tables project at Sigma.Prior to Sigma,he spent 4 years at Databricks working

3、 in both the platform and Databricks Runtime orgs.Greg OwenStaff Software EIn this lightning talk,we will cover1How Sigma provides a single pane of glass into your Lakehouse2Highlight the benefits of Input Tables,how they can improve your Databricks ML and analytics workflows3Demonstrate how organiz

4、ations can use Sigma+Databricks together to unlock ML models by using Input Tables.Using SigmaFor real-time data analysis and insightsExploratory Layer of the Modern Data StackUnlimited Ad Hoc Exploration to address unpredictable requirementsEnables teams to do their jobs backed by dataAugment Legac

5、y Solution GapsUsing SigmaWith Input TablesWhat are they?Write data to your lakehouse as easily as typing into a spreadsheetInput tables allow users to:Perform scenario modeling&what/if analysis on live dataComplete their analysis when data is missing in the warehouseMake analysis actionable by capt

6、uring business context against live analysisBetter TogetherDatabricks+SigmaLive ConnectionSecured+Governed Data Warehousing Data Science&ML Workflows&Streaming Unity Catalog Embedded Internal Analytics Input Tables Sigma AI DemoIn this ligh

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本文主要介绍了Sigma与Databricks的合作,以及使用Sigma Input Tables如何提高数据科学和机器学习的工作效率。Mitch Ertle和Greg Owen是该领域的专家,他们分别有12年和4年在Databricks的工作经验。文章提到,Input Tables可以让用户像在电子表格中输入数据一样轻松地将数据写入湖仓,支持用户在数据缺失时进行分析和模型构建,并将业务背景与实时分析相结合。Sigma与Databricks的结合,可以为用户提供包括数据仓库、数据科学和机器学习、工作流和流媒体在内的全方位服务。最后,文章邀请读者到展位#324了解如何使用Sigma和Databricks为用户赋能。
"Sigma与Databricks如何融合提升数据科学和机器学习效率?" "Input Tables能如何优化Databricks的ML和分析工作流程?" "如何通过Sigma + Databricks解锁ML模型潜力?"
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