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----
-sidebar_position: 9
-author: Wenjie Tehan
-email: wenjie.tehan@teradata.com
-page_last_update: February 8th, 2022
-description: Use AWS SageMaker with Teradata Vantage.
-keywords: [data warehouses, compute storage separation, teradata, vantage, cloud data platform, object storage, business intelligence, enterprise analytics, ai, artificial intelligence, aws sagemaker.]
----
-
-import CommunityLinkPartial from '../_partials/community_link.mdx';
-
-# Use AWS SageMaker with Teradata Vantage
-
-
-### Overview
-
-This how-to will help you to integrate Amazon SageMaker with Teradata Vantage. The approach this guide explains is one of many potential approaches to integrate with the service.
-
-Amazon SageMaker provides a fully managed Machine Learning Platform. There are two use cases for Amazon SageMaker and Teradata:
-
-1. Data resides on Teradata Vantage and Amazon SageMaker will be used for both the Model definition and subsequent scoring. Under this use case Teradata will provide data into the Amazon S3 environment so that Amazon SageMaker can consume training and test data sets for the purpose of model development. Teradata would further make data available via Amazon S3 for subsequent scoring by Amazon SageMaker. Under this model Teradata is a data repository only.
-
-2. Data resides on Teradata Vantage and Amazon SageMaker will be used for the Model definition, and Teradata for the subsequent scoring. Under this use case Teradata will provide data into the Amazon S3 environment so that Amazon SageMaker can consume training and test data sets for the purpose of model development. Teradata will need to import the Amazon SageMaker model into a Teradata table for subsequent scoring by Teradata Vantage. Under this model Teradata is a data repository and a scoring engine.
-
-The first use case is discussed in this document.
-
-Amazon SageMaker consumes training and test data from an Amazon S3 bucket. This article describes how you can load Teradata analytics data sets into an Amazon S3 bucket. The data can then available to Amazon SageMaker to build and train machine learning models and deploy them into a production environment.
-
-
-### Prerequisites
-
-import TrialDocsNote from '../_partials/teradata_trial.mdx'
-
-* Access to a Teradata Vantage instance.
-
-* IAM permission to access Amazon S3 bucket, and to use Amazon SageMaker service.
-* An Amazon S3 bucket to store training data.
-
-### Load data
-
-Amazon SageMaker trains data from an Amazon S3 bucket. Following are the steps to load training data from Vantage to an Amazon S3 bucket:
-
-1. Go to Amazon SageMaker console and create a notebook instance. See [Amazon SageMaker Developer Guide](https://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html) for instructions on how to create a notebook instance:
-
-2. Open your notebook instance:
-
-
-3. Start a new file by clicking on `New -> conda_python3`:
-
-
-4. Install Teradata Python library:
- ``` python
- !pip install teradataml
- ```
-
-5. In a new cell and import additional libraries:
- ``` python
- import teradataml as tdml
- from teradataml import create_context, get_context, remove_context
- from teradataml.dataframe.dataframe import DataFrame
- import pandas as pd
- import boto3, os
- ```
-
-6. In a new cell, connect to Teradata Vantage. Replace ``, ``, `` to match your Vantage environment:
- ``` python
- create_context(host = '', username = '', password = '')
- ```
-
-7. Retrieve data rom the table where the training dataset resides using TeradataML DataFrame API:
- ``` python
- train_data = tdml.DataFrame('table_with_training_data')
- trainDF = train_data.to_pandas()
- ```
-
-8. Write data to a local file:
- ``` python
- trainFileName = 'train.csv'
- trainDF.to_csv(trainFileName, header=None, index=False)
- ```
-
-9. Upload the file to Amazon S3:
- ``` python , id="sagemaker_first_usage", role="content-editable emits-gtm-events
- bucket = 'sagedemo'
- prefix = 'sagemaker/train'
-
- trainFile = open(trainFileName, 'rb')
- boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, localFile)).upload_fileobj(trainFile)
- ```
-
-### Train the model
-
-1. Select `Training jobs` on the left menu under `Training`, then click on `Create training job`:
-
-
-2. At the `Create training job` window, fill in the `Job name` (e.g. `xgboost-bank`) and `Create a new role` for the IAM role. Choose `Any S3 bucket` for the Amazon S3 buckets and `Create role`:
-
-
-3. Back in the `Create training job` window, use `XGBoost` as the algorithm:
-
-
-4. Use the default `ml.m4.xlarge` instance type, and 30GB of additional storage volume per instance. This is a short training job, shouldn't take more than 10 minutes.
-
-
-5. Fill in following hyperparameters and leave everything else as default:
- ```bash
- num_round=100
- silent=0
- eta=0.2
- gamma=4
- max_depth=5
- min_child_weight=6
- subsample=0.8
- objective='binary:logistic'
- ```
-
-6. For `Input data configuration`, enter the Amazon S3 bucket where you stored your training data. Input mode is `File`. Content type is `csv`. `S3 location` is where the file uploaded to:
-
-
-7. For `Output data configuration`, enter path where the output data will be stored:
-
-
-8. Leave everything else as default, and click on “Create training job”. Detail instructions on how to configure the training job can be found in [Amazon SageMaker Developer Guide](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-mkt-algo-train.html#sagemaker-mkt-algo-train-console].
-
-Once the training job's created, Amazon SageMaker launches the ML instances to train the model, and stores the resulting model artifacts and other output in the `Output data configuration` (`path//output` by default).
-
-### Deploy the model
-
-After you train your model, deploy it using a persistent endpoint
-
-### Create a model
-
-1. Select `Models` under `Inference` from the left panel, then `Create model`. Fill in the model name (e.g. `xgboost-bank`), and choose the IAM role you created from the previous step.
-2. For `Container definition 1`, use `433757028032.dkr.ecr.us-west-2.amazonaws.com/xgboost:latest` as `Location of inference code image`. `Location of model artifacts` is the output path of your training job
-
-3. Leave everything else as default, then `Create model`.
-
-### Create an endpoint configuration
-
-1. Select the model you just created, then click on `Create endpoint configuration`:
-
-
-2. Fill in the name (e.g. `xgboost-bank`) and use default for everything else. The model name and training job should be automatically populated for you. Click on `Create endpoint configuration`.
-
-### Create endpoint
-
-1. Select `Inference` -> `Models` from the left panel, select the model again, and click on `Create endpoint` this time:
-
-
-2. Fill in the name (e.g. `xgboost-bank`), and select `Use an existing endpoint configuration`:
-
-
-3. Select the endpoint configuration created from last step, and click on `Select endpoint configuration`:
-
-
-4. Leave everything else as default and click on `Create endpoint`.
-
-Now the model is deployed to the endpoint and can be used by client applications.
-
-### Summary
-
-This how-to demonstrated how to extract training data from Vantage and use it to train a model in Amazon SageMaker. The solution used a Jupyter notebook to extract data from Vantage and write it to an S3 bucket. A SageMaker training job read data from the S3 bucket and produced a model. The model was deployed to AWS as a service endpoint.
-
-### Further reading
-* [API integration guide for AWS SageMaker](https://docs.teradata.com/r/Enterprise_IntelliFlex_VMware/Teradata-VantageTM-API-Integration-Guide-for-Cloud-Machine-Learning/Amazon-Web-Services)
-* [Integrate Teradata Jupyter extensions with SageMaker notebook instance](../analyze-data/integrate-teradata-jupyter-extensions-with-sagemaker.md)
-
-
-
-
diff --git a/quickstarts/analyze-data/sagemaker-with-teradata.md b/quickstarts/analyze-data/sagemaker-with-teradata.md
new file mode 100644
index 00000000000..b7c955e427b
--- /dev/null
+++ b/quickstarts/analyze-data/sagemaker-with-teradata.md
@@ -0,0 +1,217 @@
+---
+sidebar_position: 9
+author: Wenjie Tehan, Vidhan Bhonsle
+email: developer.relations@teradata.com
+page_last_update: July 23rd, 2026
+description: Use Amazon SageMaker AI with Teradata.
+keywords: [data warehouses, compute storage separation, teradata, cloud data platform, object storage, business intelligence, enterprise analytics, ai, artificial intelligence, aws sagemaker]
+---
+
+# Use Amazon SageMaker AI with Teradata
+
+### Overview
+
+This how-to will help you to integrate Amazon SageMaker AI with Teradata. The approach this guide explains is one of many potential approaches to integrate with the service.
+
+Amazon SageMaker AI provides a fully managed Machine Learning Platform. There are two use cases for Amazon SageMaker AI and Teradata:
+
+1. Data resides on Teradata and Amazon SageMaker AI will be used for both the Model definition and subsequent scoring. Under this use case Teradata will provide data into an Amazon S3 bucket so that Amazon SageMaker AI can consume training and test data sets for the purpose of model development. Teradata would further make data available via Amazon S3 for subsequent scoring by Amazon SageMaker AI. Under this model Teradata is a data repository only.
+
+2. Data resides on Teradata and Amazon SageMaker AI will be used for the Model definition, and Teradata for the subsequent scoring. Under this use case Teradata will provide data into an Amazon S3 bucket so that Amazon SageMaker AI can consume training and test data sets for the purpose of model development. Teradata will need to import the Amazon SageMaker AI model into a Teradata table for subsequent scoring. Under this model Teradata is a data repository and a scoring engine.
+
+The first use case is discussed in this document.
+
+Amazon SageMaker AI consumes training and test data from an Amazon S3 bucket. This article describes how you can load Teradata analytics data sets into an Amazon S3 bucket. The data can then be available to Amazon SageMaker AI to build and train machine learning models and deploy them into a production environment.
+
+
+### Prerequisites
+
+import TrialDocsNote from '../_partials/teradata_trial.mdx'
+
+* Access to a Teradata instance.
+
+* IAM permissions to access the Amazon S3 bucket and use Amazon SageMaker AI.
+* An Amazon S3 bucket to store training data.
+
+### Load data
+
+Amazon SageMaker AI trains machine learning models using data stored in an Amazon S3 bucket. Follow these steps to load training data from Teradata into an Amazon S3 bucket:
+
+1. In the Amazon SageMaker AI console, select `Applications and IDEs` -> `Notebooks` -> `Notebook instances`, and then select `Create notebook instance`. See the [Amazon SageMaker AI Developer Guide](https://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html) for instructions on how to create a notebook instance:
+
+
+
+2. Open your notebook instance:
+
+
+3. Start a new file by clicking on `New -> conda_python3`:
+
+
+4. Install Teradata Python library:
+ ``` python
+ !pip install teradataml
+ ```
+
+5. In a new cell and import additional libraries:
+ ``` python
+ import teradataml as tdml
+ from teradataml import create_context, get_context, remove_context
+ from teradataml.dataframe.dataframe import DataFrame
+ import pandas as pd
+ import boto3, os
+ ```
+
+6. In a new cell, connect to Teradata. Replace ``, ``, `` to match your Teradata environment:
+ ``` python
+ create_context(host = '', username = '', password = '')
+ ```
+
+7. Retrieve data from the table where the training dataset resides using TeradataML DataFrame API:
+ ``` python
+ train_data = tdml.DataFrame('table_with_training_data')
+ trainDF = train_data.to_pandas()
+ ```
+
+ :::note
+ For the XGBoost training job used in this how-to, place the target column first and encode categorical values as numeric values before exporting the data. The CSV file must not contain a header row.
+ :::
+
+8. Write data to a local file:
+ ``` python
+ trainFileName = 'train.csv'
+ trainDF.to_csv(trainFileName, header=None, index=False)
+ ```
+
+9. Upload the file to Amazon S3. Replace `` with the name of your Amazon S3 bucket:
+
+ ```python
+ bucket = ''
+ prefix = 'sagemaker/train'
+
+ with open(trainFileName, 'rb') as trainFile:
+ boto3.Session().resource('s3') \
+ .Bucket(bucket) \
+ .Object(os.path.join(prefix, trainFileName)) \
+ .upload_fileobj(trainFile)
+ ```
+
+### Train the model
+
+1. In the Amazon SageMaker AI console, select `Model training & customization` -> `Training & tuning jobs` -> `Training jobs` from the left menu, and then select `Create training job`:
+
+
+2. In the `Create training job` window, enter a job name, such as `xgboost-bank`. For the IAM role, select an existing SageMaker execution role with access to the Amazon S3 bucket, or select `Create a new role`. When creating a new role, choose `Any S3 bucket`, and then select `Create role`:
+
+
+3. For the algorithm, select `Tabular – XGBoost : v1.3`:
+
+
+4. Under `Resource configuration`, use the following values:
+
+ * Instance type: `ml.m4.xlarge`
+ * Instance count: `1`
+ * Storage volume: `30 GB`
+ * Maximum runtime: `1 hour`
+ * Keep alive period: `0 seconds`
+
+ This is a short training job and should typically complete within 10 minutes.
+
+
+5. Enter the following hyperparameters and leave the remaining settings at their default values:
+
+ ```bash
+ num_round=100
+ verbosity=1
+ eta=0.2
+ gamma=4
+ max_depth=5
+ min_child_weight=6
+ subsample=0.8
+ objective=binary:logistic
+ ```
+
+6. Under `Input data configuration`, use the following values:
+
+ * Channel name: `train`
+ * Input mode: `File`
+ * S3 data type: `S3Prefix`
+ * Distribution: `FullyReplicated`
+ * Data format: `csv`
+ * S3 location: The Amazon S3 prefix containing `train.csv`
+
+
+7. Under `Output data configuration`, enter the Amazon S3 location where Amazon SageMaker AI will store the model artifacts:
+
+
+
+8. Leave the remaining settings at their default values and select `Create training job`. For detailed instructions on configuring a training job, see the [Amazon SageMaker AI Developer Guide](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-mkt-algo-train.html#sagemaker-mkt-algo-train-console).
+
+After the training job is created, Amazon SageMaker AI launches the ML instance and trains the model. When the training job is complete, Amazon SageMaker AI stores the resulting model artifact in the configured Amazon S3 output location:
+
+`//output/model.tar.gz`
+
+### Deploy the model
+
+After you train the model, deploy it to a real-time endpoint.
+
+### Create a model
+
+1. In the Amazon SageMaker AI console, select `Deployments & inference` -> `Deployable models` from the left menu, and then select `Create model`.
+
+2. Enter a model name, such as `xgboost-bank`, and select the IAM role created or used for the training job.
+
+3. Under `Container definition 1`, configure the following:
+
+ * For `Container input options`, select `Provide model artifacts and inference image location`.
+ * Select `Use a single model`.
+ * For `Location of inference code image`, enter the XGBoost `1.3-1` inference image URI for your AWS Region. For information about retrieving an XGBoost image URI, see [How to use SageMaker AI XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost-how-to-use.html).
+ * For `Location of model artifacts`, enter the complete Amazon S3 path to the model artifact generated by the training job:
+
+ ```text
+ //output/model.tar.gz
+ ```
+
+ * For `Model compression type`, select `CompressedModel`.
+
+
+
+4. Leave the remaining settings at their default values and select `Create model`.
+
+### Create an endpoint configuration
+
+1. Select the model you just created, and then select `Create endpoint configuration`:
+
+
+
+2. Enter a name, such as `xgboost-bank`, and leave the remaining settings at their default values. The model is automatically populated under `Variants`. Select `Create endpoint configuration`.
+
+### Create endpoint
+
+1. In the Amazon SageMaker AI console, select `Deployments & inference` -> `Deployable models` from the left menu. Select the model again, and then select `Create endpoint`:
+
+
+
+2. Enter a name, such as `xgboost-bank`, and select `Use an existing endpoint configuration`:
+
+
+
+3. Select the endpoint configuration created in the previous step, and then select `Select endpoint configuration`:
+
+
+
+4. Leave the remaining settings at their default values and select `Create endpoint`.
+
+Wait until the endpoint status changes to `InService`. The model is then deployed to the endpoint and can be used by client applications.
+
+:::caution
+A real-time endpoint incurs charges while it is running. Delete the endpoint when you no longer need it.
+:::
+
+### Summary
+
+This how-to demonstrated how to extract training data from Teradata and use it to train a model in Amazon SageMaker AI. The solution used a Jupyter notebook to extract data from Teradata and upload it to an Amazon S3 bucket. An Amazon SageMaker AI training job read the data from the Amazon S3 bucket and produced a model. The model was then deployed to an Amazon SageMaker AI real-time endpoint.
+
+### Further reading
+
+* [API integration guide for AWS SageMaker](https://docs.teradata.com/r/Enterprise_IntelliFlex_VMware/Teradata-VantageTM-API-Integration-Guide-for-Cloud-Machine-Learning/Amazon-Web-Services)
+* [Integrate Teradata Jupyter extensions with SageMaker notebook instance](../analyze-data/integrate-teradata-jupyter-extensions-with-sagemaker.md)
\ No newline at end of file
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