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train ai and ml models google cloud data agent kit extension for visual studio code google cloud documentation skip to main content technology areas close ai and ml application development application hosting compute data analytics and pipelines databases distributed hybrid and multicloud industry solutions migration networking observability and monitoring security storage cross product tools close access and resources management costs and usage management infrastructure as code sdk languages frameworks and tools console english deutsch español américa latina français indonesia italiano português brasil עברית 中文 简体 中文 繁體 日本語 한국어 sign in data agent kit extension visual studio code start free overview guides resources technology areas more overview guides resources cross product tools more console discover data agent kit extension for vs code overview get started install the extension set up and configure settings find and explore data query data develop transform data train ai and ml 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the launch stage descriptions vs code keyboard_arrow_down vs code antigravity ide learn how to train ai and ml models in the google cloud data agent kit extension for visual studio code in this quickstart you use a session template and a sample jupyter notebook to predict new york city taxi tip amounts using a remote jupyter kernel with pyspark you try out various models like linear regression random forest and xgboost this process lets you perform distributed training and inference it demonstrates scalability across multiple machines using spark ml and the xgboost library although not covered in this quickstart there are multiple ways to train ai and ml models using google cloud data agent kit extension for visual studio code if your training dataset is large or you want the distributed training capabilities that apache spark offers you can use spark notebooks with remote kernels if your dataset is in bigquery and bigquery ml supports your use case you can use a bigquery dataframes notebook if your dataset is small and you want to train your model locally you can use a python notebook before you begin before you begin do the following install the extension configure extension settings review the guidance at find and explore data create a spark runtime template serverless spark runtime templates let you start an apache spark session with a given set of configurations to create a new serverless runtime template complete the following steps in the ide activity bar click the google cloud data agent kit icon in the google cloud data agent kit menu expand apache spark expand serverless and then click create serverless runtimes a serverless runtime creation form appears in the display name field enter ai ml tutorial go to the auto scaling section set spark dynamicallocation enabled to false in the drop down list this setting is needed for xgboost to work with apache spark leave all other fields set to the default click submit create a new notebook note in this quickstart you use a prebuilt sample notebook you can also use the agent in your ide to generate a spark notebook with a prompt that describes your specific ml use case next create a new spark notebook under apache spark in the google cloud data agent kit tab click new spark notebook choose remote kernel for the kernel type click start with a sample notebook in the list of samples select data science with pyspark and distributed xgboost an untitled jupyter notebook appears train your model in the notebook tab click run all the kernel picker asks you to select a kernel to execute the notebook with click select another kernel click remote spark kernels select the ai ml tutorial on serverless spark the runtime template that you created earlier note on windows long path support must be enabled to work with spark notebooks on remote kernels you see the following notification while the system creates your serverless spark session connecting to kernel ai ml tutorial on serverless spark when the notebook connects to the remote pyspark kernel execution starts at the first cell this process takes approximately two to three minutes key point managed service for apache spark executes your training code entirely the system doesn t download any part of your training dataset to your local machine inspect your spark session in the google cloud data agent kit tab under apache spark expand the ai ml tutorial runtime template the ide displays the list of interactive sessions that you have created with this runtime template locate the session that the system created by executing the notebook at the top of the list click the session to see its details you can review the session configuration and the resources that the system consumed to execute your notebook clean up after successfully executing the notebook perform the following cleanup steps in the google cloud data agent kit tab under apache spark right click serverless and select list serverless runtimes the list of serverless runtimes appears click the action menu for ai ml tutorial to list all the interactive sessions that the system created from your template under actions click delete go back to the serverless runtimes window under actions for ai ml tutorial click delete click confirm to delete the template that you created for this tutorial what s next managed service for apache spark overview transform data send feedback except as otherwise noted the content of this page is licensed under the creative commons attribution 4 0 license and code samples are licensed under the apache 2 0 license for details see the google developers site policies java is a registered trademark of oracle and or its affiliates last updated 2026 07 10 utc need to tell us more easy to understand easytounderstand thumb up solved my problem solvedmyproblem thumb up other otherup thumb up hard to understand hardtounderstand thumb down incorrect information or sample code incorrectinformationorsamplecode thumb down missing the 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