This page covers on how to manage and use dataapps.

DataApps

DataApps are interactive visualizations built using React, designed for four main problem types within Rapid Model Recipes: Binary Classification, Regression, Multi-Class Classification, and Binary Experimental.

You can create two types of DataApps: Model DataApps and Custom DataApps.

Model DataApps: These are dependent on a trained model and can only be created if a project includes one. They focus on visualizing the performance and predictions of the specific model in your project.

Custom DataApps: These are not tied to a specific model and support a variety of inputs, making them flexible for different data sources. Custom DataApps can be built using:

• Project Canvas Datasets: Use all datasets available on the project canvas, including any user-uploaded datasets. This option also allows you to use AskAI to explore these datasets interactively.

• Uploaded Datasets: You can apply AskAI to analyze any datasets you upload, providing a straightforward way to explore your data without needing a model.

• SQL Sources: Connect directly to MySQL databases through pre-configured connectors, allowing you to fetch and analyze data directly from these sources using AskAI.

• Documents and PDFs: Allows you to upload documents in formats like PDF, DOC, and DOCX directly into the DataApp. Once uploaded, the Ask AI feature allows users to query the document contents, enabling quick extraction of insights, information analysis, and text-based responses.

• Prediction services: Allows you to select a pre-configured prediction service linked to your project. This allows you to query the AI model directly through the Ask AI interface.

• Job: Allows you to select input datasets, define output datasets, and access reports generated after running the job. This enables you to run queries on the selected file through the AskAI interface.

On the other hand, the DataApps can also be built by writing Python code on Juypter Notebook. If the DataApps are built from Notebook, business users can consume those once are deployed by data scientists. The data scientist will first create an app template on the Juypter Notebook and uses this app template to deploy the dataapp passing a project ID, scenario, artifact, or model. Business users can only view and delete the dataapps published onto the UI from the Notebook, but cannot create, modify the app templates. All the app templates created in a tenant are saved in the Data app module.

If you want to generate a chart, you can either create a custom template on the Notebook or use an existing system template. Examples of system templates you can use to generate the chart after building the machine learning flows are - Correlation Matrix, EDA data drift, Pivot Table viewer, Visual Analytics, Pandas Profiler, and so on. Using a system or custom template, you can generate the charts only with the default Streamlit version. Contrary to this, dataapps support all Streamlit versions.

The app template is similar to system templates, but app templates can only be used to generate dataapps(charts).

Accessing and Creating a Model DataApp at a project level

Use this procedure to create a model DataApp in a project. After you run the data pipeline for any of these four problem types such as binary classification, regression, multi-class classification, or binary experimental problems, the DataApp button gets enabled.

1. Click the menu icon and select Projects

2. Select the project for which you want to create DataApps and click the DataApps icon from the project level navigation.

3. Click the plus icon. This takes you to the Create DataApp page.

4. Specify this information:

DataApp Name:

• The name of the DataApp.

DataApp Description:

• The description of the DataApp.

Recipe Name:

• The recipe for which you want to create a DataApp.

Environment:

By default, the environment selected during project creation is applied. However, you have the flexibility to choose a different environment for running your DataApps.

Choose Image:

• Click to upload an image to display on the DataApp card that you see on the DataApps page.

5. Enter Model controls in the text box to provide specific context to the AI guide. This helps in aligning the AI-generated responses more closely with the user’s particular use case

6. Click Create.

You can see the DataApp card created in this project.

Viewing dataapps in a tenant

Use this procedure to view the dataapps created on the UI or published by Notebook users from Notebook.

1. Click the menu icon and select DataApps. This displays the dataapps dashboard where you can see all the DataApps for different projects in this tenant.

Note

The DataApps will become inactive after a certain period set by the admin. If you want to use DataApps, you must relaunch.

2. Click Relaunch on the DataApp card that you want to launch again.

3. Click on the Dataapp. The screen displays the following tabs:

Feature importance - Assigning scores to input features based on how helpful they are in predicting a target variable. These scores are ranked to help users understand which features have a significant impact on model prediction.

Model performance - This evaluates the performance of the model.

What-If analysis - Help data scientists to get insights into what a model predicts for given input values. This allows them to experiment with various combinations of values for key features and observe the resulting predictions.

You can use the sliders to change the values or use drop-down to select values and click Predict What-If Outcome to review what the model predicts for the given input values.

Prediction - You can perform predictions on the uploaded dataset. For more information, see Performing predictions on the uploaded dataset.

Performing predictions on the uploaded dataset

Use this procedure to do the predictions on the new dataset and generate charts using Ask AI.

1. Click Browse to upload the dataset and obtain predictions generated by the model.

2. Click Upload File From Local. Once you upload the file, click Close to upload.

2. Click Generate Prediction. Once the prediction is done on the dataset, you can download the prediction results, using the Download option.

Note

You can use the Log option to view the detailed records of the execution activity and identify issues.

3. Click Ask AI button.

Important

Apart from generating charts, you can also perform data transformation operations on the dataset by prompting for the Ask AI.

4. Click +DataApp to add this chart to the dataapp. The Update Name box appears where you can provide the custom name for the chart and click Save.

5. Click the User Charts tab to view this chart.

Ask AI - You can use the Ask AI feature similar to what you see in AI-assisted recipe to provide text prompt in the chat window and generate the chart outputs on the dataset used for model building. This allows you to generate visualizations and carry out different data pre-processing operations on the selected dataset.

You can add as many chats as you want and switch between the chat windows from the Chat list on the left. The difference between the chat window used for prediction output dataset and dataset used for model building can be see in its chat name on the left.

Perform the following actions clicking the Actions drop-down:

• Copy the dataapps URL to share with the other business users, using the Copy option.

• Open the dataapps on a new tab, using the Open in New Tab option.

• Configure the shutdown time of the DataApp, using the Config option.

• View logs of DataApps to debug issues, using the Logs option.

• Delete the DataApp that is no longer required, using the Delete option.

• Customize the branding appearance of your DataApp, using the Branding option. This opens the Branding modal. Enter your desired title in the DataApp Title field, then use the Upload from Computer option to upload your logo. Once you have added the title and logo, click Save to apply the changes. The customized title and logo will now appear on the AskAI page and will also be visible when the DataApp is opened in a new tab.

Creating Custom DataApp using project Canvas datasets as input

Use this procedure to create a custom DataApp. Note that these DataApps are model-independent, so no project model is required to create them.

1. Click the menu icon and select Projects

2. Select the project for which you want to create DataApps and click the DataApps icon from the project level navigation.

3. Click the plus icon. This takes you to the Create DataApp page.

4. Specify this information:

DataApp Name:

The name of the Dataapp.

DataApp description:

The description of the Dataapp.

Environment:

By default, the environment selected during project creation is applied. However, you have the flexibility to choose a different environment for running your DataApps.

5. Click Choose Image to browse and select the image that is visible on the DataApp cards.

6. Select an input from the Inputs section. Possible options:

   • Project Canvas Datasets - Use the datasets available in the project and uploaded by the user.

   • Custom Uploaded Datasets - Use the datasets that are uploaded by the user.

   • SQL Sources - Use the data that is fetched from the SQL databases.

   • Documents and PDFs - Use the datasets uploaded in pdf, doc, or docx formats.

   • Prediction service - Use the prediction service as input

Here we are selecting Project Canvas Datasets as an input option.

7. Select a model that you want to use in the Custom DataApp. Possible values:

   • OpenAI GPT-4 Turbo

   • OpenAI GPT-4o (by default this is selected)

   • Azure OpenAI GPT-4o

   • Anthropic Claude 3.5 Sonnet (beta)

8. Toggle ON the Enable Response Caching option to return the same response for the identical queries despite asking multiple times.

9. Select Access Control to manage access to chats and charts generated within the DataApp. The available options are:

• Team Access: Allows all users within the tenant to view chats and charts in the DataApp.

• Individual Access: Restricts visibility of chats and charts to users who created them. For example, if User A creates Chat A and Chart C, only User A can view these.

10. Select the Security options. Possible values:

    • Sample Data : The sample of 5 rows of data is shared with the LLM for context.

    • Only Metadata: Only the column names in the dataset are shared with the LLM for context.

11. Enter Model controls in the text box to provide specific context to the AI guide. This helps in aligning the AI-generated responses more closely with the user’s particular use case

12. Click Create.

13. Click on the DataApp card to launch the AskAI DataApp. The AskAI chat window appears.

14. Select the Dataset on which you want to create the DataApp. You can view all the datasets that are on the canvas in the drop-down.

Note

You can also view the +Upload New Datasets option in the datasets drop-down, from where you can upload a new dataset on which you want to run the query using AskAI. Alternatively, click the Select from Artifacts tab to choose a document from the Artifacts folder. You can also use the search box to quickly locate the document you need.

15. Enter the query in the query box. You can use slash to choose the type of output you want the AI to generate, such as text, dataset, or chart. Once the output is generated, you can:

• Copy the answer using the Copy Answer option.

• View the generated code by selecting the View Code option.

• Get an explanation for each line of code by using the Explain option, which becomes visible only after clicking View Code.

If you have generated a chart using the Ask AI, then you can view the + DataApp option.

16. Click + DataApp to add this chart to the dataapp. Clicking this opens the Update Name box where you can provide the custom name for the chart and click Save.

Once a chart is added, you can easily track the total number of charts added under the User Charts tab. An indicator on this tab displays the number of charts.

17. Click the User Charts tab to view this chart.

Creating Custom DataApp using Datasets as an input

Use this procedure to create a custom DataApp using Datasets an an input. Note that these DataApps are model-independent, so no project model is required to create them.

1. Click the menu icon and select Projects

2. Select the project for which you want to create DataApps and click the DataApps icon from the project level navigation.

Note

You can also view the +Upload New Datasets option in the datasets drop-down, from where you can upload a new dataset on which you want to run the query using AskAI.

3. Click the plus icon. This takes you to the Create DataApp page.

4. Specify this information:

DataApp Name:

The name of the Dataapp.

DataApp description:

The description of the Dataapp.

Environment:

By default, the environment selected during project creation is applied. However, you have the flexibility to choose a different environment for running your DataApps.

5. Click Choose Image to browse and select the image that is visible on the DataApp cards.

6. Select an input as Custom Uploaded datasets.

7. Select a model that you want to use in the Custom DataApp. Possible values:

   • OpenAI GPT-4 Turbo

   • OpenAI GPT-4o (by default this is selected)

   • Azure OpenAI GPT-4o

   • Anthropic Claude 3.5 Sonnet (beta)

8. Toggle ON the Enable Response Caching option to return the same response for the identical queries despite asking multiple times.

9. Select Access Control to manage access to chats and charts generated within the DataApp. The available options are:

• Team Access: Allows all users within the tenant to view chats and charts in the DataApp.

• Individual Access: Restricts visibility of chats and charts to users who created them. For example, if User A creates Chat A and Chart C, only User A can view these.

10. Select the Security options. Possible values:

    • Sample Data : The sample of 5 rows of data is shared with the LLM for context.

    • Only Metadata: Only the column names in the dataset are shared with the LLM for context.

11. Enter Model controls in the text box to provide specific context to the AI guide. This helps in aligning the AI-generated responses more closely with the user’s particular use case

12. Click Create. The DataApp card is created.

13. Click on the DataApp card to launch the AskAI DataApp. The AskAI chat window appears.

14. Click Select Dataset to Start in the prompt box drop-down and select +Upload New Datasets. This opens the Upload Dataset(s) window.

15. Click Upload Files From Local to upload the file and then click Upload. Once done, click Done.

16. Enter the query in the query box. You can use slash to choose the type of output you want the AI to generate, such as text, dataset, or chart. Once the output is generated, you can:

• Copy the answer using the Copy Answer option.

• View the generated code by selecting the View Code option.

• Get an explanation for each line of code by using the Explain option, which becomes visible only after clicking View Code.

If you have generated a chart using the Ask AI, then you can view the + DataApp option.

17. Click + DataApp to add this chart to the dataapp. Clicking this opens the Update Name box where you can provide the custom name for the chart and click Save.

Once a chart is added, you can easily track the total number of charts added under the User Charts tab. An indicator on this tab displays the number of charts.

18. Click the User Charts tab to view this chart.

Note

You can upload and run queries on as many datasets as you want. To upload a new dataset, click the plus button next to Charts on the left side.

Creating Custom DataApp using SQL database as an input

Use this procedure to create a custom DataApp using SQL database an an input. Note that these DataApps are model-independent, so no project model is required to create them.

1. Click the menu icon and select Projects

2. Select the project for which you want to create DataApps and click the DataApps icon from the project level navigation.

Note

You can also view the +Upload New Datasets option in the datasets drop-down, from where you can upload a new dataset on which you want to run the query using AskAI.

3. Click the plus icon. This takes you to the Create DataApp page.

4. Specify this information:

DataApp Name:

The name of the Dataapp.

DataApp description:

The description of the Dataapp.

Environment:

By default, the environment selected during project creation is applied. However, you have the flexibility to choose a different environment for running your DataApps.

5. Click Choose Image to browse and select the image that is visible on the DataApp cards.

6. Select an input as SQL Sources.

7. Select a model that you want to use in the Custom DataApp. Possible values:

   • OpenAI GPT-4 Turbo

   • OpenAI GPT-4o (by default this is selected)

   • Azure OpenAI GPT-4o

   • Anthropic Claude 3.5 Sonnet (beta)

8. Toggle ON the Enable Response Caching option to return the same response for the identical queries despite asking multiple times.

9. Select Access Control to manage access to chats and charts generated within the DataApp. The available options are:

• Team Access: Allows all users within the tenant to view chats and charts in the DataApp.

• Individual Access: Restricts visibility of chats and charts to users who created them. For example, if User A creates Chat A and Chart C, only User A can view these.

10. Select the Security options. Possible values:

    • Sample Data : The sample of 5 rows of data is shared with the LLM for context.

    • Only Metadata: Only the column names in the dataset are shared with the LLM for context.

11. Enter Model controls in the text box to provide specific context to the AI guide. This helps in aligning the AI-generated responses more closely with the user’s particular use case

12. Click Create. The DataApp card is created.

13. Click on the DataApp card to launch the AskAI DataApp. The AskAI chat window appears.

14. Click Select Data Connector drop-down in the prompt box. This opens the Select Data Connector window.

15. Select the connector from which you can to fetch the data.

Note

You need to first configure the SQL connectors on the Connectors page to view here in the connectors list.

16. Click Next. This opens the Select Table from Connector window.

17. Select the check boxes corresponding to the tables from which you want to fetch data. Only four tables can be selected at a time.

18. Click Connect to connect to the connector and fetch data. You can view the data fetched from the table on the chat window.

18. Enter the query in the query box. You can use slash to choose the type of output you want the AI to generate, such as text, dataset, or chart. Once the output is generated, you can:

• Copy the answer using the Copy Answer option.

• View the generated code by selecting the View Code option.

• Get an explanation for each line of code by using the Explain option, which becomes visible only after clicking View Code.

If you have generated a chart using the Ask AI, then you can view the + DataApp option.

19. Click + DataApp to add this chart to the dataapp. Clicking this opens the Update Name box where you can provide the custom name for the chart and click Save.

Once a chart is added, you can easily track the total number of charts added under the User Charts tab. An indicator on this tab displays the number of charts.

20. Click the User Charts tab to view this chart.

Note

You can upload and run queries on as many datasets as you want. To upload a new dataset, click the plus button next to Charts on the left side.

Creating Custom DataApp using Documents and PDFs as an input

Use this procedure to create a custom DataApp using documents and PDFs an an input. Note that these DataApps are model-independent, so no project model is required to create them.

1. Click the menu icon and select Projects

2. Select the project for which you want to create DataApps and click the DataApps icon from the project level navigation.

3. Click the plus icon. This takes you to the Create DataApp page.

4. Specify this information:

DataApp Name:

The name of the Dataapp.

DataApp description:

The description of the Dataapp.

Environment:

By default, the environment selected during project creation is applied. However, you have the flexibility to choose a different environment for running your DataApps.

5. Click Choose Image to browse and select the image that is visible on the DataApp cards.

6. Select an input as Documents and PDFs.

7. Toggle ON the Enable Response Caching option to return the same response for the identical queries despite asking multiple times.

8. Select Access Control to manage access to chats and charts generated within the DataApp. The available options are:

• Team Access: Allows all users within the tenant to view chats and charts in the DataApp.

• Individual Access: Restricts visibility of chats and charts to users who created them. For example, if User A creates Chat A and Chart C, only User A can view these.

9. Enter Model controls in the text box to provide specific context to the AI guide. This helps in aligning the AI-generated responses more closely with the user’s particular use case

10. Select a model that you want to use in the Custom DataApp. Possible values:

    • OpenAI GPT-4 Turbo

    • OpenAI GPT-4o (by default this is selected)

11. Click Create. The DataApp card is created.

12. Click on the DataApp card to launch the AskAI DataApp. The AskAI chat window appears.

13. To upload documents, click Upload Documents, which supports formats such as PDF, DOC, DOCX, TXT, and Markdown. This option allows you to browse and upload files directly from your local system. Once uploaded, you can begin asking questions using AskAI.

The Recent Documents tab displays recently uploaded documents, while the Select from Artifacts tab shows a list of folders in the Artifacts library. Clicking on a folder shows the files within it, allowing you to select the desired file for querying. However, only artifact files in PDF, DOC, DOCX, TXT, or Markdown formats can be selected for this purpose.

14. Enter the query in the query box. The generated output will be in the text format. You can:

• Copy the answer using the Copy Answer option.

• View the generated code by selecting the View Code option.

• Get an explanation for each line of code by using the Explain option, which becomes visible only after clicking View Code.

Creating Custom DataApp using Prediction Service as Input Type

Use this procedure to create a custom DataApp using prediction service an an input.

1. Click the menu icon and select Projects

2. Select the project for which you want to create DataApps and click the DataApps icon from the project level navigation.

3. Click the plus icon. This takes you to the Create DataApp page.

4. Specify this information:

DataApp Name:

The name of the Dataapp.

DataApp description:

The description of the Dataapp.

Environment:

By default, the environment selected during project creation is applied. However, you have the flexibility to choose a different environment for running your DataApps.

5. Click Choose Image to browse and select the image that is visible on the DataApp cards.

6. Select an input as Prediction Service.

7. Select Access Control to manage access to chats and charts generated within the DataApp. The available options are:

• Team Access: Allows all users within the tenant to view chats and charts in the DataApp.

• Individual Access: Restricts visibility of chats and charts to users who created them. For example, if User A creates Chat A and Chart C, only User A can view these.

8. Click Create. The DataApp card is created and redirects you to the AskAI tab.

9. Select the prediction service from the list. You can only view the prediction services created in this project. Once the prediction service is selected, you can start asking the questions to the AskAI

10. Enter the query in the query box. The generated output will be in the text format. You can:

• Copy the answer using the Copy Answer option.

• View the generated code by selecting the View Code option.

• Get an explanation for each line of code by using the Explain option, which becomes visible only after clicking View Code.

Creating Custom DataApp using Job as an input

Use this procedure to create a custom DataApp using job an an input. Note that these DataApps are model-independent, so no project model is required to create this type of custom DataApp.

Note

Ensure that at least one job has been created and run successfully, with datasets in a built state. Without a successful job run, the datasets will not be available for selection or for executing prompts

1. Click the menu icon and select Projects

2. Select the project for which you want to create DataApps and click the DataApps icon from the project level navigation.

3. Click the plus icon. This takes you to the Create DataApp page.

4. Select an input as Job. Based on the selected input, the fields change.

5. Specify this information:

DataApp Name:

The name of the Dataapp.

DataApp description:

The description of the Dataapp.

Environment:

By default, the environment selected during project creation is applied. However, you have the flexibility to choose a different environment for running your DataApps.

6. Click Choose Image to browse and select the image that is visible on the DataApp cards.

7. Select a model that you want to use in the Custom DataApp. Possible values:

   • OpenAI GPT-4o (by default this is selected)

   • OpenAI GPT-4 Turbo

   • Azure OpenAI GPT-4o

   • Anthropic Claude 3.5 Sonnet (beta)

8. Select the job on which you want to create the DataApp from the list of jobs created in the project.

9. Toggle ON the Enable Response Caching option to return the same response for the identical queries despite asking multiple times.

10. Select Access Control to manage access to chats and charts generated within the DataApp. The available options are:

• Team Access: Allows all users within the tenant to view chats and charts in the DataApp.

• Individual Access: Restricts visibility of chats and charts to users who created them. For example, if User A creates Chat A and Chart C, only User A can view these.

11. Select the Security options. Possible values:

    • Sample Data : The sample of 5 rows of data is shared with the LLM for context.

    • Only Metadata: Only the column names in the dataset are shared with the LLM for context.

12. Enter Model controls in the text box to provide specific context to the AI guide. This helps in aligning the AI-generated responses more closely with the user’s particular use case

13. Click Create. The DataApp card is created.

14. Click on the DataApp card to launch the AskAI DataApp. The AskAI chat window appears.

15. Click Select Dataset to Start in the prompt box drop-down and select the dataset you want. All the datasets you are viewing in the drop-down are the input and output datasets generated after the latest job run.

Once you select the dataset, this loads the data onto the chat window.

16. Enter your query in the query box and use a slash (/) to select the type of output you want the AI to generate, such as text, dataset, or chart.

Once the output is generated, you can:

• Copy the answer using the Copy Answer option.

• View the generated code by selecting the View Code option.

• Get an explanation for each line of code by using the Explain option, which becomes visible only after clicking View Code.

You can also choose from prompt suggestions tailored to the selected dataset.

• If you select prompt suggestions, AskAI will provide five relevant prompts. You can either copy a suggestion into the query box or run it directly using the Run button.

• If you have an existing AskAI chat created to query input and output datasets or other job-generated outputs, the chat will continue to reference datasets from the previous job run. To query the latest job inputs and outputs, you will need to create a new chat each time.

17. Click the Job Charts tab to add charts generated after the job run in this tab.

18. Click Add Charts From Job to add charts. You can choose the charts you want to add to the DataApp if there are multiple charts generated.

19. Click Add Chart To DataApp next to the chart you want to add. However, if you no longer want this chart in the Job Charts tab, you can use Remove From DataApp to remove it.

Note

Charts are displayed only if the data pipeline generates chart outputs. Furthermore, once a chart output is created after running a query, it cannot be edited like other DataApp input types—for example, modifying the title or changing colors.

Dataapps dashboard

Dataapps dashboard has all the dataapps in the form of cards. These datapps are either created in projects or published from the Notebook. Each card gives various details such as project name, description of the project, recipe used, the person who created the dataapp, last shutdown by, and the date and time at which the dataapp was created. You can also launch the app and relaunch when the app is idle.

Use the search box to search for Dataapp templates by name.

The list view provides the Data app details in the table form. You can view the same details that you see in the tile view of Dataapps such as DataApp name, Description, Project, Recipe, Input type, Last Shutdown by, Updated by, Updated on and launch status of the app.

Use the table settings icon to rearrange the columns in the order you want and view or hide the columns by selecting and clearing the check boxes next to the columns.

The card view provides all the data app cards on the dashboard. Click on each card to view the DataApp associated with the project.

Use the filter option to filer the dataapps by project and view only the dataapps associated with the filtered project on the dataapps dashboard. You cannot view this option in project-level DataApps.

1. Select or clear the projects whose dataapps you want to view or hide.

2. Click Apply.

Creating a DataApp

Use this procedure to create a DataApp. Depending on your business requirements, you can create different types of custom or model-based DataApps.

• From the Projects Page: DataApps created here are specific to the respective project.

• From the DataApps Page: You can create DataApps for any project within the tenant.

To create a DataApp:

1. Click the menu icon and select DataAapps. This shows the DataApps created under this tenant and in different projects.

2. Click the plus button located in the top-right corner of the page, whether you are in the List View or Card View.

3. Select a project in which you want to create the DataApp. This displays the list of projects available within the project.

4. Click Next to proceed to the Create DataApp page, where you can choose the type of DataApp you want to create. The available types include:

   • Project Canvas Datasets. To create a custom DataApp using project canvas datasets as input, see Creating Custom DataApp using project Canvas datasets as input.

   • Custom Uploaded Datasets. To create a custom DataApp using custom uploaded datasets as input, see Creating Custom DataApp using Datasets as an input.

   • SQL Sources. To create a custom DataApp using SQL source as an input, see Creating Custom DataApp using SQL database as an input.

   • Documents and PDFs. To create a custom DataApp using documents and pdfs as an input, see Creating Custom DataApp using Documents and PDFs as an input.

   • Prediction Service. To create a custom DataApp using prediction service as an input, see Creating Custom DataApp using Prediction Service as Input Type.

   • Job. To create a custom DataApp using job as an input, see Creating Custom DataApp using Job as an input.

   • Model DataApp. To create a custom DataApp using model as an input, see Accessing and Creating a Model DataApp at a project level.

Editing a DataApp

Use this procedure to delete a DataApp.

To edit a DataApp:

1. Click the menu icon and select DataAapps. This shows the DataApps created under this tenant and in different projects.

2. Click on the ellipses icon on a DataApp that you want to edit and select the Edit option. This opens the side panel window to make changes to DataApp details.

3. Modify various details of your DataApp, including the name, description, environment settings, models, security configurations, response caching, and model controls. However, please note that the input type chosen during the creation of the DataApp cannot be edited.

3. Click Save to update the changes.

Deleting a DataApp

Use this procedure to delete a DataApp.

To delete a DataApp:

1. From the left navigation menu, select DataAapps. The DataApps dashboard is displayed.

2. Click the ellipses icon on the DataApps dashboard.

3. Click Delete. The dialog box appears prompting you to delete or discard

4. Click Yes, Delete to delete the DataApp permanently.

Viewing DataApp logs

Use the history of DataApp logs to debug the issues in DataApp.

To view DataApp logs:

1. From the left navigation menu, select DataAapps. The DataApps dashboard is displayed.

2. Click the ellipses icon on the DataApps dashboard.

3. Click Logs. The logs side panel appears. You can search for the log data you want in the search box.

You can also click on the kebab menu to view two options:

• Select Export to export the logs to the text file.

• Select Open in New Tab to open the logs in a new tab.

Configuring DataApp Shutdown Time

Use this procedure to configure the shutdown time for DataApp. You can also check resource consumption while DataApp is running.

To view DataApp logs:

1. From the left navigation menu, select DataAapps. The DataApps dashboard is displayed.

2. Click the ellipses icon on the DataApps dashboard.

3. Click Configs.

The Configuration & Consumption side-panel appears.

4. Select the time period (in hours) after which the DataApp should automatically shut down if it remains inactive.

5. Select Evergreen check box to keep the DataApp Running always.

6. Clear this check box to specify the time after which the DataApp must shut down if there is no activity. You can set the time in hours.

7. Click Save.