Develop and Deploy an NLP Model (2023)

Note:

Consider providing more datasets in the project and task management domain to improve model training and prediction accuracy.

You will now learn about the model-building processes using the example Jupyter notebook. Follow these steps:

1. Set up Jupyter notebook

   Set up the Jupyter notebook in a conda environment defined in the environment YAML file for this solution.

2. Prepare dataset

   Use the sample dataset provided in a CSV file. Review and analyze the data to enable you to create a good machine learning model. Include additional data in the project or task management domain if required.

3. Process dataset

   See Also

   5 Steps to Create a Basic Machine Learning Model using PythonThe 7 Key Steps To Build Your Machine Learning ModelRandom Forest Regression in Python Sklearn with Example - MLK - Machine Learning Knowledge30+ Best Python Project Ideas -Easy, Intermediate, and Advanced Ideas - 2023 - Machine Learning Projects
   Process your dataset using the framework provided which makes it easier for you to load and process the data. This includes tokenization, splitting the training and validation data, and numericalization.

4. Perform statistical analysis

   Analyze the proportion of reports with each status label; Red, Amber, and Green. For example, look for the most frequently occurring words in Red status. Using the analysis, you can find a way to make a classifier based on it called Naive Bayes.

5. Create NLP model using FastAI

   Use FastAI to create a language model based on the vocabulary of the dataset. Then use a transfer learning technique to integrate the language model with the core model (AWD_LSTM).

6. Test and validate your model

   Use some sample data to test your model.

7. Create a model binary file

   Export the model to a pickle file (pkl) and upload it to the OCI Data Science platform.

After you have the model binary file, you must package the binary file together with the model runtime and custom logic Python script (score.py).

You can now upload it to the Oracle Cloud Infrastructure Data Science (ODS) platform and expose it as a REST API.

Use the model deployment Python script (ModelDeployment.py) to learn how to use the OCI Python SDK and create and deploy the model to ODS. Additionally use the included example custom logic Python script to load serialized model objects to the memory, and define an inference endpoint, predict().

The model deployment Python script performs the following steps to deploy the model:

1. Retrieves the existing model from Model Catalog and Model Deployment.
2. Deactivates the existing model in Model Deployment.
3. Deletes the existing model artifact from Model Catalog.
4. Uploads the artifacts export.pkl, runtime.yaml and score.py in a compressed ZIP format to Model Catalog.
5. Creates or updates Model Deployment.

Modify the following Python script and provide the correct parameter values for your environment:

compartmentID="<YOUR OCI COMPARTMENT OCID>" projectID="<YOUR ODS PROJECT OCID>" modelDisplayName="Risk Predictor" modelDescription="Risk Predictor Sample" modelDeploymentDisplayName="Risk Predictor" modelDeploymentDescription="Risk Predictor Sample" modelDeploymentInstanceCount=1 modelDeploymentLBBandwidth=10 #mbps modelDeploymentPreditLogID="<YOUR ODS DEPLOYMENT PREDICT LOG OCID>" modelDeploymentPreditLogGroupID="<YOUR ODS DEPLOYMENT PREDICT LOG GROUP OCID>" modelDeploymentAccessLogID="<YOUR ODS DEPLOYMENT ACCESS LOG OCID>" modelDeploymentAccessLogGroupID="<YOUR ODS DEPLOYMENT ACCESS LOG GROUP OCID>" modelDeploymentInstanceShapeName="<OCI VM SHAPE , FOR EXAMPLE: VM.Standard2.1>"

Alternatively, you can update the parameters from the OCI Console.

Follow the instructions in About Model Catalog in the Oracle Cloud Infrastructure Data Science documentation linked in the Explore section.

You can choose the Virtual Machine (VM) shape when you deploy the model. See the Oracle Cloud Infrastructure Data Science Models and VM Shapes documentation linked in the Explore section to find the supported shapes.

Follow these instructions to create a project and populate the ODS Project ID:

1. In the OCI Console, open the compartment you used to create the ODS Project.
2. In the navigation menu, under Analytics & AI, click Data Science.
3. Click Create project to create a Data Science project.
4. After the project is created, copy the ODS Project OCID, and paste it in the Python script parameter under project ID.

For Model Deployment Predict and Access Logs, you must first create a log group and then create a log shared by both events.

Follow these steps:

1. In the OCI Console, confirm that you are viewing the compartment you used to create the ODS Project.
2. In the navigation menu, under Observability & Management, click Log Groups.
3. Click Create Log Group to create a log group for the model deployment. The predict and access log can use the same log group.
4. After the log group is created, copy the log group OCID, and paste it in the Python script parameter under modelDeploymentPreditLogGroupID and modelDeploymentAccessLogGroupID.
5. While you are still in the Logging service screen, click Logs.
6. Click Create custom log to create a custom log and select the log group you created earlier.
7. After the log is created, copy the log OCID, and paste it in the Python script parameter under modelDeploymentPreditLogID and modelDeploymentAccessLogID.

You can now develop and deploy your functions to complete the implementation of this solution.

You implement multiple API calls to backend applications and perform data aggregation, filtering, and manipulation.

OCI Health Checks does the following:

• Calls Oracle Functions at the end of each day.
• Gets the task progress using the Oracle Project Management REST API.
• Identifies the project manager using the Oracle Cloud HCM REST API.
• Retrieves the OSN task wall conversation and the text comments posted by the project team member.

Oracle Functions then posts the prediction about the project status to the project manager's OSN wall.

For the purposes of this example, we have used Java as the language and the Apache HttpClient library to connect to the REST Service. This example uses the Apache library because it's easy to use and implement. Alternatively, you can use the new HTTP client that Java 11 provides. You also use the OCI Java SDK to retrieve the resource principal and the secret stored in Oracle Cloud Infrastructure Vault.

The sample Functions Java code executes the following steps:

1. Retrieve the credential/secret from Oracle Cloud Infrastructure Vault.
2. Retrieve the project details from Oracle Project Management.
3. Retrieve the project manager person ID from HCM.
4. Retrieve the username of the project manager in HCM.
5. Retrieve the project manager wall details in OSN.
6. Retrieve all tasks for the project using the project ID.
7. For each project and task:
   1. Retrieve the social object ID using the task external ID.
   2. Retrieve the latest comment in OSN.
   3. Send the comment to the Oracle Cloud Infrastructure Data Science platform to predict the sentiment.

Store Credentials in Oracle Vault

Create an app

fn create app RiskPredictor --annotation oracle.com/oci/subnetIds='["<subnet-ocid>"]'

Configure Environment Parameters

fn config app RiskPredictor FA_BASE_URL <ORACLE PROJECT MANAGEMENT BASE URL>fn config app RiskPredictor ODS_REST_API <ODS MODEL ENDPOINT>fn config app RiskPredictor PROJECT_ID_LIST <PROJECT ID IN ORACLE PROJECT MANAGEMENT>fn config app RiskPredictor FA_USER <ORACLE PROJECT MANAGEMENT USER NAME>fn config app RiskPredictor OCI_REGION <OCI REGION>fn config app RiskPredictor FA_SECRET_OCID <FA SECRET OCID>