Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 2: Data preparation and model building with Amazon SageMaker Canvas

Artificial Intelligence Part 1 covered the Snowflake database setup and established the foundational infrastructure for this no-code machine learning (ML) workflow.

Part 2 of this blog series covers complete data preparation and model building workflow using Amazon SageMaker Canvas, demonstrating how to connect directly to Snowflake data sources, transform and prepare data using Data Wrangler’s visual transformations, and build a fraud detection model using the XGBoost algorithm.

Amazon SageMaker Canvas is a visual, no-code machine learning service that enables business analysts and domain experts to build accurate ML models and generate predictions. Amazon SageMaker Canvas provides an intuitive interface for data preparation, model training, and prediction generation democratizing access to machine learning across organizations while maintaining enterprise security and governance.

Solution overview #

This solution guides you through the complete workflow of preparing data and building a machine learning model using Amazon SageMaker Canvas, with direct integration to your Snowflake data warehouse.

Prerequisites

Complete Part 1 setting up your Snowflake environment before starting this post. You need the Snowflake account credentials and connection details from Part 1 to complete the steps in this post.

Amazon SageMaker Canvas setup

Open the AWS Management Console and search for Amazon SageMaker Canvas. Select it from the list, or press Enter.

Create your Amazon SageMaker domain

A domain provides the foundational organizational unit for your Amazon SageMaker environment. It serves as a dedicated workspace that houses user profiles, storage configurations, and security settings. Each domain delivers isolated resources and access controls for managing team collaboration and data governance.

• Navigate to environment configurations in the left pane and choose Domains. - Choose Create domain. - Choose

Set up for single user (Quick setup) to automatically create both your domain and user profile.

Launch Amazon SageMaker Canvas

• Select Canvas from the left-hand pane. - Choose the domain and user profile you created.
• Choose Open Canvas. - Wait 3–5 minutes while Canvas prepares your workspace.

Data Wrangler: ML data preparation #

Amazon SageMaker Data Wrangler simplifies data preparation for machine learning workflows. With built-in transformations and an intuitive visual interface, Data Wrangler reduces the time traditionally spent on data preparation and analysis. For industries ranging from financial services to healthcare, this capability unlocks significant value so subject matter experts can directly prepare their data for analysis.

The integration with Snowflake reduces data movement challenges, so users can connect directly to their Snowflake data warehouses, transform the data within Canvas, and proceed straight to model building. This unified, no-code environment accelerates time-to-insight while maintaining data governance and security.

A. Data connection and initial setup

In this section, you connect Amazon SageMaker Canvas to a Snowflake data source.

• Navigate to Amazon SageMaker Canvas and choose Data Wrangler from the left navigation pane. ChooseImport and prepare, then choose** Tabular**to work with structured datasets.

• Specify the source of your tabular dataset. From the data source menu, choose** Snowflakeas your connection type. Then choose Add Connection**to establish the link between Amazon SageMaker Canvas and your Snowflake environment. With this integration, you can access your cloud data warehouse directly within Canvas. It avoids manual data exports and makes sure you are always working with the most current data in your Snowflake instance.

• In the Snowflake connection pop-up menu, provide the following:

• A connection name.

• The Account ID, set to your Snowflake organization value, plus a hyphen, plus the Snowflake account ID. - The username for the Snowflake account you set up earlier.

• The password that you set previously.

• After the connection is established, you will create card-level outlier thresholds to identify unusual spending patterns for each credit card and category combination. This helps the model detect when a transaction amount significantly deviates from a cardholder’s typical behavior. Copy the SQL query to prepare the fraud detection dataset.

• Run the query:

• Choose Edit in SQL in the top right corner to open the SQL editor. - Paste the SQL query into the editor.

• Choose Run SQL to run and preview results. - Choose Import to bring the dataset into your Canvas workspace.

• Choose

B. Data enrichment

In the following steps, you will enrich the dataset with transaction-level features and merchant-specific fraud indicators.

• Choose Data flow to add more data. In the top right corner, chooseAdd Data, and then choose** Tabular**.

• Choose the data source as Snowflake and the existing connection you created before.

• In the Edit SQL window, paste the SQL query. This query provides temporal patterns (hour of day, day of week), demographic information (age categories), and merchant-level outlier thresholds, all critical signals for fraud detection. ChooseRun SQL, then choose** Import**.

C. Data transformation

In this section, you will join the data together.

• On the Data flow screen, you see the two data sources imported from Snowflake. Choose the firstdata type and choose the plus icon next to it. ChooseCombine data, then** Join**. When the right panel opens, choose the second** data type**from the data flow to join.

• Choose Left outer join for theJoin type. Add a** Join keyand choose Category**as the left and right join key.

• Choose Preview to view the joined data. ChooseAdd to proceed.

Next, you will create outlier features and remove sensitive columns.

• Return to the Data flow screen, choose the join, choose the plus sign (+) next to it, and then choose** Add transform**.

• After the interface opens, choose Add transform in the right-hand panel, and then chooseCustom Formula.

• This option gives you the flexibility to add columns using a Spark SQL expression. Copy and paste the following Formula on the Home tab. Enter the output column asCC_FLAG

and chooseAdd.

• To add another column, repeat the same steps to enter a custom formula, then copy and paste this query and name the output column as MERCHANT_AMT_FLAG

.

• To make sure the model remains free of sensitive information such as card numbers, merchant names, and outlier amounts tied to cards or merchants, remove these columns using built-in transformation.
• Select Add transform and chooseManage Columns - Select the Transform type toDrop column - Select the columns you want to remove from your dataset
• Choose Add to apply the transformation.CC_NUM

.AMT_OUTLIER

.MERCHANT

.MERCHANT_AMT_OUTLIER

.

• Select

D. Quality analysis and model export

With data preparation complete, you will run a quality analysis report to get insights into the data. The insights report identifies common data issues, such as target leakage or class imbalance, helping users address them early in the workflow.

• To initiate the analysis, choose the Analyses tab. In the right-hand panel, chooseData Quality and Insights Report from theAnalysis type list. - Choose IS_FRAUD

as thetarget column. This tells Canvas which variable you want to predict. Then choose the** Classificationoption under Problem type**. The** Data sizeshould remain as Sampled Dataset**. Finally, choose** Create**to launch the analysis.

• Within a few minutes, a detailed analysis report will be created which includes a quick summary of the data, feature summary, duplicate rows, anomalous samples and much more. In the Quick model section, review the accuracy metrics in the training and validation datasets. A confusion matrix follows the accuracy statistics. The idea is to use this report after any data engineering to observe how it impacts model quality.

• The feature summary section shows feature importance. In practice, if there are features with low prediction power, you might choose to drop those features.

Export to model building

You’ve now combined two data sources, engineered new features, removed unnecessary ones, and previewed your model’s potential accuracy by running the analysis. With data preparation complete, it’s time to build your predictive model.

• To begin, return to the Data flow tab, choose the plus sign (+) next to your final transform, and then choose** Create model**.

• Choose a descriptive name under Model name, and then choose** Export and create model**. The export process may take a few minutes as Canvas processes your entire dataset in real time.

After a few minutes, the Build screen opens up.

• Choose IS_FRAUD

as theTarget Column. - Choose Configure model underModel type. Select2-category model asmodel type.

• Next, select Ensemble as the training method withXGBoost as the algorithm, a strategic choice that balances accuracy with efficiency.

• Deselect the FRAUD_HISTORY andMERCHANT_FRAUD_HISTORY columns, then chooseStandard build to start training. The model takes approximately 15–30 minutes to complete.

• With the model training complete, navigate to the Analyze tab to review the results. Here, you can examine which features had the most impact on predictions and explore the scatterplot and charts to understand relationships between data values and fraud classification.

• Choose Advanced Metrics to further understand model performance.

• Next, use the trained model to make predictions on an unseen dataset. Download the sample prediction CSV file.Navigate to thePredict tab. ChooseManual, and then choose** Create Dataset**.

• Upload the dataset, choose Preview dataset, and then choose** Create dataset**. After the dataset loads, choose the dataset and choose** Generate Predictions**. The model takes a few minutes to make the predictions. Wait until the status of the job changes to** Ready**. - To analyze the results visually using Amazon Quick Sight, you must first verify the following prerequisites (detailedhere):- Verify AWS Region alignment: Your Quick Sight account must be set up in the same AWS Region as your Amazon SageMaker Canvas domain.

• Add Amazon Quick Sight permissions to your Amazon SageMaker execution role: The AWS Identity and Access Management (IAM) execution role attached to your Amazon SageMaker domain needs additional permissions to send predictions to Amazon Quick Sight. Navigate to the IAM console, find the execution role associated with your Amazon SageMaker domain (created during setting up Amazon SageMaker domainat the very beginning), and add the required inline policy as describedhere. - Grant Quick Sight access to the Amazon SageMaker S3 bucket: Navigate to Quick Sight, go to Manage Accounts, then choose** AWS Resourcesfrom the left-hand navigation pane. Make sure Amazon Simple Storage Service (Amazon S3) is selected and choose the appropriate S3 bucket that has the predictions generated by Amazon SageMaker Canvas, named sagemaker-{region}-{account_id}. - Add Quick Sight users: Make sure the users you want to share predictions with have been added to your Quick Sight account with an Author orAdmin** role. Go toManage Quick Sight and navigate toManage users to invite new users or verify existing ones. For details, seeManaging user access. You will enter their usernames when sending predictions.

• Next, select the Job name and chooseSend to Amazon Quick Sight.

• In the new window, add the users who were previously granted Amazon Quick Sight permissions as viewers of the dashboard, and then choose Send.

Conclusion #

Part 2 covered the complete data preparation and model building workflow in Amazon SageMaker Canvas from connecting to Snowflake data sources and engineering features using Data Wrangler’s visual transformations, to joining multiple data sources, analyzing data quality, and training a fraud detection model all without requiring machine learning programming expertise. With the trained model now generating predictions on unseen data, the foundation is set for Part 3, where those ML-driven insights are brought to life through interactive dashboards in Amazon Quick Sight.

References #

• Part 1 – Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 1: Setting up your Snowflake environment - Part 3 – Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 3: Visualizing insights with Amazon Quick Sight