Training and Deploying Your First Model

This tutorial is an introduction to some of the primary features of AnalyticOps. The tutorial covers basic tasks that are related to Git-based XGBoost Python Demo Model training, evaluation, approval and deployment within multiple environments.

In this tutorial, you will complete the following tasks:

Using the XGBoost Python Demo Model

XGBoost is an implementation of gradient boosted decision trees designed for speed and performance that is dominative competitive machine learning.

(Comment: Some introduction of XGBoost Python Demo Model will go here)

Train a Model Version

To train an XGBoost Python Demo model version:

  1. From List of Projects, open the Demo project.

    The List of Models displays.

  2. For the Python Diabetes Prediction model, click the View Model Versions icon.

    The Python Diabetes Prediction model versions page displays.

  3. Click the Train Model button.

    The Train Model Version dialog displays.

    https://docs.tdaoa.com/images/train_dialog.png
  4. In the Basic tab, set the properties:

    Property

    Value

    Dataset Template

    PIMA Diabetes

    Dataset

    PIMA Diabetes Train

    Connection

    Vantage Demo

    Hyper Parameters

    eta

    0.2

    max_depth

    6

    https://docs.tdaoa.com/images/train_basic.png
  5. In the Advanced tab, set the properties:

    Property

    Value

    Engine

    Docker Image

    Resource Template

    S Standard

    https://docs.tdaoa.com/images/train_advanced.png
  6. Click Train.

    The model version training progress displays on the screen as below.

    Note: For more information about Training job progress, see Model Versions in AnalyticOps User Guide.

  7. Click Done when the training progress completes.

    The trained model version adds to the list of model versions.

    The Training Details display on the model version lifecycle page.

See Also

Evaluate Model Version

To initiate the evaluation of a trained XGBoost Python Demo model version, two methods can be followed.

First Method

  1. From the Python Diabetes Prediction model versions, select the Trained version of the model.

    The Actions icon displays on the top.

  2. Click the Actions icon to see the menu. Select Evaluate.

    https://docs.tdaoa.com/images/evaluate_action_1.png

Second Method

  1. From the Python Diabetes Prediction model versions, select the View Events icon for the Trained version of the model.

    The Model version lifecycle page displays.

  2. Click the Evaluate button.

    The Evaluate Model Version dialog displays.

    https://docs.tdaoa.com/images/evaluate_dialog.png
  3. In the Basic tab, set the properties:

    Property

    Value

    Dataset Template

    PIMA Diabetes

    Dataset

    PIMA Diabetes Evaluate

    Connection

    Vantage Demo

    https://docs.tdaoa.com/images/evaluate_basic.png
  4. In the Advanced tab, set the properties:

    Property

    Value

    Engine

    Docker Image

    Resource Template

    S Standard

    https://docs.tdaoa.com/images/evaluate_advanced.png
  5. Click Evaluate.

    The model version evaluation progress displays on the screen as below.

    Note: For more information about Evaluation job progress, see Model Lifecycle in AnalyticOps User Guide.

    https://docs.tdaoa.com/images/evaluate_progress.png
  6. Click Done when the evaluation progress completes.

    The model version status is changed to Evaluated.

    The Evaluation Details display 0n the model version lifecycle page.

See Also

Approve Model Version

To initiate the approval of an evaluated XGBoost Python Demo model version, two methods can be followed.

First Method

  1. From the Python Diabetes Prediction model versions, select the Evaluated version of the model.

    The Actions icon displays on the top.

    Comment: Screen for Approval flow from Model versions list will display here. The feature is not available.

  2. Click the Actions icon to see the menu. Select Approve.

Second Method

  1. From the Python Diabetes Prediction model versions, select the View Events icon for the Evaluated version of the model.

    The Model version lifecycle page displays.

  2. Click the Approve button.

    The Approve Model Version dialog displays.

    https://docs.tdaoa.com/images/approval_comments.png
  3. Insert Comments and click Approve.

    The model version status is changed to Approved.

    The Approval Details display on the model version lifecycle page.

Note: For more information about Approval and Rejection of model version, see Model Approval in AnalyticOps User Guide.

See Also

Deploy Model Version as RESTFUL

To deploy an approved XGBoost Python Demo model version, two methods can be followed.

First Method

  1. From the Python Diabetes Prediction model versions, select the Approved version of the model.

    The Actions icon displays on the top.

  2. Click the Actions icon to see the menu. Select Deploy.

    https://docs.tdaoa.com/images/deploy_action_1.png

Second Method

  1. From the Python Diabetes Prediction model versions, select the View Events icon for the Approved version of the model.

    The Model version lifecycle page displays.

  2. Click the Deploy button.

    The Deploy Model Version dialog displays.

    https://docs.tdaoa.com/images/deploy_dialog.png
  3. In the Basic tab, set the properties:

    Property

    Value

    Engine Type

    RESTful

    Replicas

    1

    https://docs.tdaoa.com/images/deploy_basic.png
  4. In the Advanced tab, set the properties:

    Property

    Value

    Engine

    Docker Image

    Resource Template

    S Standard

    https://docs.tdaoa.com/images/deploy_advanced.png
  5. Click Deploy.

    The model version deployment progress displays on the screen as below.

    Note: For more information about Deployment job progress, see Model Lifecycle in AnalyticOps User Guide.

  6. Click Done when the deployment progress completes.

    The model version status changes to Deployed.

    The Deployment Details display on the model version lifecycle page.

See Also

Deploy Model Version as Batch

To deploy an approved or deployed XGBoost Python Demo model version, two methods can be followed.

First Method

  1. From the Python Diabetes Prediction model versions, select the Deployed version of the model.

    The Actions icon displays on the top.

  2. Click the Actions icon to see the menu. Select Deploy.

    https://docs.tdaoa.com/images/deploy_batch_action_1.png

Second Method

  1. From the Python Diabetes Prediction model versions, select the View Events icon for the Deployed version of the model.

    The Model version lifecycle page displays.

  2. Click the Deploy button.

    The Deploy Model Version dialog displays.

    https://docs.tdaoa.com/images/deploy_batch_dialog.png
  3. In the Basic tab, set the properties:

    Property

    Value

    Engine Type

    Batch

    Dataset Template

    PIMA Diabetes

    Connextion

    Vantage Demo

    Schedule

    Hourly

    Hours

    1

    Mins

    0

    https://docs.tdaoa.com/images/deploy_batch_basic.png
  4. In the Advanced tab, set the properties:

    Property

    Value

    Engine

    Docker Image

    Resource Template

    S Standard

    https://docs.tdaoa.com/images/deploy_batch_advanced.png
  5. Click Deploy.

    The model version deployment progress displays on the screen as below.

    Note: For more information about Deployment job progress, see Model Lifecycle in AnalyticOps User Guide.

  6. Click Done when the deployment progress completes.

    The model version status remains as Deployed.

    The Deployment Details display on the model version lifecycle page.

See Also

Deploy Model Version in Vantage

To deploy an approved or deployed XGBoost Python Demo model version as Vantage, two methods can be followed.

First Method

  1. From the Python Diabetes Prediction model versions, select the Deployed version of the model.

    The Actions icon displays on the top.

  2. Click the Actions icon to see the menu. Select Deploy.

    https://docs.tdaoa.com/images/deploy_vantage_action_1.png

Second Method

  1. From the Python Diabetes Prediction model versions, select the View Events icon for the Deployed version of the model.

    The Model version lifecycle page displays.

  2. Click the Deploy button.

    The Deploy Model Version dialog displays.

    https://docs.tdaoa.com/images/deploy_vantage_dialog.png
  3. In the Basic tab, set the properties:

    Property

    Value

    Engine Type

    In-Vantage

    Connection

    Vantage Demo

    Database

    AOA_DEMO

    https://docs.tdaoa.com/images/deploy_vantage_basic.png
  4. In the Advanced tab, set the properties:

    Property

    Value

    Engine

    Docker Image

    Resource Template

    S Standard

    Build Properties

    LANGUAGE

    PMML

    https://docs.tdaoa.com/images/deploy_vantage_advanced.png
  5. Click Deploy.

    The model version deployment progress displays on the screen as below.

    Note: For more information about Deployment job progress, see Model Lifecycle in AnalyticOps User Guide.

  6. Click Done when the deployment progress completes.

    The model version status remains as Deployed.

    The Deployment Details display on the model version lifecycle page.

See Also

Retire All Deployments

To retire the active deployments for XGBoost Python Demo model version, two methods can be followed.

First Method

  1. From the Python Diabetes Prediction model versions, select the Deployed version of the model.

    The Actions icon displays on the top.

  2. Click the Actions icon to see the menu. Select Retire.

    https://docs.tdaoa.com/images/retire_action_1.png

Second Method

  1. From the Python Diabetes Prediction model versions, select the View Events icon for the Deployed version of the model.

    The Model version lifecycle page displays.

  2. Click the Retire button.

    The Retire Model Version dialog displays.

    https://docs.tdaoa.com/images/retire_dialog.png

Retire RESTful Deployment

  1. Select RESTful Deployment ID.

    https://docs.tdaoa.com/images/retire_basic.png
  2. Click Retire.

    The model version retirement progress displays on the screen as below.

    Note: For more information about Retiring job progress, see Model Lifecycle in AnalyticOps User Guide.

  3. Click Done when the retirement progress completes.

    The model version status remains as Deployed as the model version has still active deployments.

    The Retirement Details display on the model version lifecycle page.

Retire Batch Deployment

  1. Select the Batch Deployment ID in the Retire Model Version dialog.

    https://docs.tdaoa.com/images/retire_batch_basic.png
  2. Click Retire.

    The model version retirement progress displays on the screen as below.

    Note: For more information about Retiring job progress, see Model Lifecycle in AnalyticOps User Guide.

  3. Click Done when the retirement progress completes.

    The model version status is Deployed as the model version still has active deployments.

    The Retirement Details display on the model version lifecycle page.

Retire Vantage Deployment

  1. Select the Vantage Deployment ID in the Retire Model Version dialog.

    https://docs.tdaoa.com/images/retire_vantage_dialog.png
  2. Click Retire.

    The model version retirement progress displays on the screen as below.

    Note: For more information about Retiring job progress, see Model Lifecycle in AnalyticOps User Guide.

  3. Click Done when the retirement progress completes.

    The model version status is changed to Retired since there is no active deployment for the version.

    The Retirement Details display on the model version lifecycle page.

See Also