# 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 > Source: > Published: 2026-08-20 21:23:29+00:00 [Artificial Intelligence](/blogs/machine-learning/) # 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 [Part 1](/blogs/machine-learning/build-a-no-code-ml-workflow-with-snowflake-amazon-sagemaker-canvas-and-amazon-quick-part-1-setting-up-your-snowflake-environment/) 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](/sagemaker/ai/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](/blogs/machine-learning/build-a-no-code-ml-workflow-with-snowflake-amazon-sagemaker-canvas-and-amazon-quick-part-1-setting-up-your-snowflake-environment/) 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](https://docs.aws.amazon.com/sagemaker/latest/dg/onboard-quick-start.html)) 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](/sagemaker/ai/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. Choose**Import and prepare**, then choose** Tabular**to work with structured datasets. - Specify the source of your tabular dataset. From the **data source menu**, choose** Snowflake**as 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, choose**Add 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. Choose**Run 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 first**data type** and choose the plus icon next to it. Choose**Combine data**, then** Join**. When the right panel opens, choose the second** data type**from the data flow to join. - Choose **Left outer join** for the**Join type**. Add a** Join key**and choose** Category**as the left and right join key. - Choose **Preview** to view the joined data. Choose**Add** 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 choose**Custom 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 as`CC_FLAG` and choose**Add**. - 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 choose**Manage Columns** - Select the **Transform** type to**Drop 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](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-data-insights.html) 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, choose**Data Quality and Insights Report** from the**Analysis type** list. - Choose `IS_FRAUD` as the**target column**. This tells Canvas which variable you want to predict. Then choose the** Classification**option under** Problem type**. The** Data size**should 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 the**Target Column**. - Choose **Configure model** under**Model type.** Select**2-category model** as**model type**. - Next, select **Ensemble** as the training method with**XGBoost** as the algorithm, a strategic choice that balances accuracy with efficiency. - Deselect the **FRAUD_HISTORY** and**MERCHANT_FRAUD_HISTORY** columns, then choose**Standard 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.](https://aws-blogs-artifacts-public.s3.us-east-1.amazonaws.com/artifacts/ML-20655/prediction_dataset.csv)Navigate to the**Predict** tab. Choose**Manual**, 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](/quick/quicksight/), you must first verify the following prerequisites (detailed[here](https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-send-predictions.html)):- 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 domain](https://docs.aws.amazon.com/sagemaker/latest/dg/onboard-quick-start.html)at the very beginning), and add the required inline policy as described[here](https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-quicksight-permissions.html). - Grant Quick Sight access to the Amazon SageMaker S3 bucket: Navigate to Quick Sight, go to **Manage Accounts**, then choose** AWS Resources**from 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** or**Admin** role. Go to**Manage Quick Sight** and navigate to**Manage users** to invite new users or verify existing ones. For details, see[Managing user access](https://docs.aws.amazon.com/quick/latest/userguide/managing-user-access-qs-iam.html). You will enter their usernames when sending predictions. - Next, select the **Job name** and choose**Send 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](/blogs/machine-learning/build-a-no-code-ml-workflow-with-snowflake-amazon-sagemaker-canvas-and-amazon-quick-part-3-visualizing-insights-with-amazon-quick-sight/), 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](/blogs/machine-learning/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](/blogs/machine-learning/build-a-no-code-ml-workflow-with-snowflake-amazon-sagemaker-canvas-and-amazon-quick-part-3-visualizing-insights-with-amazon-quick-sight/)