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Predict project risk sentiment using NLP and machine learning

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 1. Architecture Center
 2. Solution Playbooks


PREDICT PROJECT RISK SENTIMENT USING NLP AND MACHINE LEARNING

Back to Architecture Center
Get Started
 * 
   Learn About Predicting Project Risk Sentiment
   * Architecture
   * Before You Begin
   * Considerations
   * About Required Services and Roles

Develop
 * 
   Develop and Deploy an NLP Model
   * Develop a Model
   * Upload and Deploy the Model
   * Create an ODS Project
   * Create a Log Group and Log
   * 
     Develop Functions
     * Store Credentials in Oracle Vault
     * Create an app
     * Configure Environment Parameters

Explore
 * Explore More

Download Code
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Acknowledgments
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 1. Predict project risk sentiment using NLP and machine learning
 2. Learn About Predicting Project Risk Sentiment


LEARN ABOUT PREDICTING PROJECT RISK SENTIMENT

Project managers must capture task progress from team members to accurately
predict impacts to the project schedule and communicate progress and risks to
management.

Project managers have conversations with project team members to learn about the
task status and any impacts to the project schedule. For example, when a team
member knows that a task will be delayed, they must communicate this to the
project manager. If they don't, the project manager remains unaware of the
impacts to the project schedule. Project managers can have conversations with
team members using Oracle Social Network (OSN).

This solution is an example of how to use a combination of REST APIs, Natural
Language Processing (NLP), and machine learning to predict project risk
sentiment and enable project managers to be proactively notified of project
schedule impacts. You can predict the risk sentiment using Oracle Fusion Cloud
Project Management REST APIs and a machine learning model deployed in the Oracle
Cloud Infrastructure Data Science platform and post a Red-Amber-Green (RAG)
status prediction on the project manager's OSN wall.

You can learn more about the technology and the process used for this solution
in the following video:




ARCHITECTURE

The architecture shows how you can use REST APIs to capture progress reported by
project team members and predict impacts to the project schedule.


Description of the illustration project-task-risk-nlp.png

The flow for tracking task progress and predicting impact on project schedule
are as follows:

 * Project team members report their progress and inform the project manager in
   case of a delay using Oracle Social Network (OSN) at the end of the day.
 * Oracle Project Management REST API gets the project and task progress and the
   last conversation on the OSN task wall.
 * OCI Data Science (ODS) endpoint is invoked and runs the prediction model
   using Oracle Functions.
 * The Oracle Cloud HCM REST API identifies the project manager.
 * Oracle Functions gets the project manager's credentials required to post the
   prediction on their OSN wall from Oracle Cloud Infrastructure Vault.
 * Oracle Functions posts the prediction about the project schedule impact to
   the project manager's wall.

This architecture supports the following components:

 * Oracle Fusion Cloud Project Management: Enables organizations to plan and
   track their projects, assign the right talent, balance capacity against
   demand, and scale resources up or down quickly as needs change. This solution
   uses the REST API to get the task comment on the OSN wall reported by the
   project team member at the end of the day.
 * Oracle Fusion Cloud Human Capital Management: Enables organizations to plan,
   manage, and help optimize global people processes with one common data
   source. The Oracle Cloud HCM REST API identifies the project manager of the
   project.
 * Oracle Social Network (OSN): OSN is a secure private network that provides a
   broad range of social tools designed to capture and preserve information
   flowing between people, enterprise applications, and business processes. OSN
   is tightly integrated with project and task management in Oracle Fusion Cloud
   Project Management to enable collaboration among project team members.
 * Functions
   
   Oracle Functions is a fully managed, multitenant, highly scalable, on-demand,
   Functions-as-a-Service (FaaS) platform. It is powered by the Fn Project open
   source engine. Functions enable you to deploy your code, and either call it
   directly or trigger it in response to events. Oracle Functions uses Docker
   containers hosted in Oracle Cloud Infrastructure Registry.

 * Health Checks
   
   Oracle Cloud Infrastructure Health Checks monitors and alerts you on the
   availability of any public-facing service hosted in Oracle Cloud
   Infrastructure (OCI). It provides high frequency external monitoring to
   determine the availability and performance of any publicly facing service,
   including hosted websites, API endpoints, or externally facing load
   balancers. You can immediately be aware of any availability issue affecting
   your customers.

 * Data Science
   
   Oracle Cloud Infrastructure Data Science is a fully managed, serverless
   platform that data science teams can use to build, train, and manage machine
   learning (ML) models on Oracle Cloud Infrastructure (OCI). It can easily
   integrate with other OCI services such as Oracle Autonomous Data Warehouse,
   Oracle Cloud Infrastructure Object Storage, and more. You can build and
   evaluate high-quality machine learning models that increase business
   flexibility by putting enterprise-trusted data to work quickly, and you can
   support data-driven business objectives with easier deployment of ML models.


BEFORE YOU BEGIN

OCI Data Science provides many prebuilt conda environments. If you cannot find
the environment that you require for your data science project, you have an
option to create a custom environment.

Before you begin, follow either of these options to create your conda
environment:

 * Install a conda environment in OCI Data Science
 * Create a custom conda environment

Review and download the samples provided on GitHub.


CONSIDERATIONS

To ensure the prediction model achieves good results, you must build a language
model that understands the specific language used by project managers.

You will use the transfer learning technique to take the weights learned by a
model trained on a large, general dataset, and then use the weights as the
starting point to train a new model on a different dataset. This new model will
be deployed to the Oracle Cloud Infrastructure Data Science platform as one of
the key components to predict the text comments entered by project team members.


ABOUT REQUIRED SERVICES AND ROLES

This solution requires the following services:

Services Privilege Required to... Oracle Project Management Manage Project
Service
 * Project Integration Specialist (ORA_PJF_PROJECT_INTEGRATION_SPECIALIST_JOB)
 * Project Application Administrator
   (ORA_PJF_PROJECTS_APPLICATION_ADMINISTRATOR_JOB)

Oracle Cloud HCM Use REST Services Human Capital Management Integration
Specialist (ORA_HRC_HUMAN_CAPITAL_MANAGEMENT_INTEGRATION_SPECIALIST_JOB)

This solution requires the following services and roles:

Service Name: Role Required to... Fusion Applications Suite: Administrator
Manage and configure security and application in Fusion Applications Suite.
Oracle Cloud Infrastructure: Administrator Deploy functions, configure OCI
Health Checks, Policies, and create the Data Science platform. Oracle Identity
Cloud Service: Security Administrator and Application Administrator Manage
identity providers and applications.

See Learn how to get Oracle Cloud services for Oracle Solutions to get the cloud
services you need.

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Title and Copyright Information

Predict project risk sentiment using NLP and machine learning

F54677-01

January 2023

Copyright © 2023, Oracle and/or its affiliates. 


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