Tech Stack : Node.js, Chatbot, IBM Watson , Elastic Search, Cloud
In this code pattern, learn how to create a Node.js chatbot application that uses Watson Assistant and Elasticsearch. The chatbot application, which the user interacts with from a mobile app, can run on either Kubernetes or Cloud Foundry.
OPTION 1 : Project
Industry Mentor from CEW will be assigned to help on the project.
Project Lifecycle will be : Scope, Architecture & Planning, Design, Coding, Testing, Go-Live/Award
Technology Involved : Node.js, Chatbot, IBM Watson , Elastic Search, Cloud , Agile, Functional & Non Functional Requirements Capturing, Architecture & Solution Design, Project Plan, Project Estimatation, Use Case Modelling, UML Design, Process Flow Diagrams, UX Personas, Stakeholder Analysis, UX Best Practices, Responsive Design, Coding Best Practices, Unit Testing, Github, Deployment of Project, Devops, Automation, Go Live Procedures + this project
(limited time offer)
OPTION 2 : Class & Project
Industry Expert Teachers from CEW will be assigned for 12 weeks coaching on the technologies used in project.
After 12 weeks of teaching, project work will start.
Industry Mentor from CEW will be assigned to help on the project.
Project Lifecycle will be : Scope, Architecture & Planning, Design, Coding, Testing, Go-Live/Award
Technology Involved : Node.js, Chatbot, IBM Watson , Elastic Search, Cloud , Agile, Functional & Non Functional Requirements Capturing, Architecture & Solution Design, Project Plan, Project Estimatation, Use Case Modelling, UML Design, Process Flow Diagrams, UX Personas, Stakeholder Analysis, UX Best Practices, Responsive Design, Coding Best Practices, Unit Testing, Github, Deployment of Project, Devops, Automation, Go Live Procedures + this project
(limited time offer)
No Other Class in the world teaches you Real life implementation | Agile Implementation | Requirements Capturing | Architecture & Solution Design | Project Plan | Project Estimatation | Use Case Modelling | UML Design | Process Flow Diagrams | UX Personas | Stakeholder Analysis | UX Best Practices | Responsive Design | Coding Best Practices | Unit Testing | Github | Deployment of Project | Devops | Automation |Go Live Procedures + this project of the projects like we do
What you will learn ?
- Real-world how IT projects are implemented
- Implement project using Node.js, Chatbot, IBM Watson , Elastic Search, Cloud
- Capture Requirements of the project using Use Case Modelling (Stakeholders, Personas, Main Scanario, Alternate, Negative, Edge Cases)
- Define Functional & Non-Functional Use Cases
- Create project design using UML Modelling
- Implement project coding using code respositories.
- How Google Analytics, Search Engine Optimization(SEO) are implemented.
- How UX Banners are created.
- Testing using unit tests(create & execute)
- Deployment of the project in cloud
- User Acceptance Testing - How client identify issues, how you fix issues
- Go Live of the project
Description
This code pattern is based on the chatbot that is being used for the IBM Developer mobile application, available in your mobile App Store.
The chatbot that you build with this code pattern uses TV shows data from TV Maze to make recommendations and provide show information.
After completing this pattern, you will understand how to:
1. Create a chatbot, from end to end
2. Deploy and run a Node.js application on Kubernetes or Cloud Foundry
FLOW
Flow for Cloud Foundry
1. The user interacts with the chatbot from the React UI of the mobile application by asking a question via text.
2. The React UI sends the user’s message to the Node.js backend on Cloud Foundry.
3. The Node.js backend sends the message to Watson Assistant to determine the intent and entities of the user’s message.
4. The Node.js backend queries the Elasticsearch database based on the intents and entities processed by Watson Assistant.
5. The response and results are sent to the React UI.
Flow for Kubernetes
1. The user interacts with the chatbot from the React UI of the mobile application by asking a question via text.
2. The React UI sends the user’s message to the Node.js backend on Kubernetes.
3. The Node.js backend sends the message to Watson Assistant to determine the intent and entities of the user’s message.
4. The Node.js backend queries the Elasticsearch database based on the intents and entities processed by Watson Assistant.
5. The response and results are sent to the React UI.
Timelines : 8 weeks
Project will go through the phases of scope, design, coding, unit testing, UAT, Award(Go Live)
Winner will be chosen for each phase(scope,design,coding,unit testing,UAT,Go-Live) of the project, cash prize from CEW ′, certificate, cloud credits will be provided for each phase.
