Draft outline 2026 - up1/training-courses GitHub Wiki

1. หลักสูตรเกี่ยวกับการใช้งาน Container

• ความรู้ของ Containers ตั้งแต่ปูพื้น Basic จนถึงระดับ intermediate (ถ้ามี lab หรือ hand on ด้วยน่าจะดี)
• หัวข้อการใช้งานเบื้องต้น มีเรื่อง Docker, Kubernetes เบื้องต้น

Software Requirements

• Docker desktop
• Kubernetes on devlopment or local machine
  • Minikube
  • Kubectl
• VS Code

Day 1

• Introduction to Docker
  • Why container ?
  • Container principles
  • Manage container with Docker
  • Basic components
    • Image
    • Container
    • Registry
  • Build image with Dockerfile
  • Working with Docker compose
  • Monitoring and Observability
  • Deploy container with Docker swarm (Cluster mode)

Day 2

• Introduction to Kubernetes
  • Manage container with Kubernetes
  • Kubernetes components
    • Pods
    • Service
    • Deployment and ReplicaSet
    • Ingress and Gateway API
    • HPA (Horizontal Pod Autoscaler)
  • Monitoring and Observability

2. Course :: Develop RESTful API with .NET 8 C#

Software requirements

• VS Code
• .NET in Visual Studio Code
• .NET 8 SDK
• Postman

Check .NET SDK in command line

$dotnet --version

Course Outline

• Create project with .NET

  • Create solution and project
    • Working with dotnet cli
      • Web API
      • Testing with xunit
    • Basic project structure
    • Download dependency with nuget

• Essential features of C# to develop REST API

  • Data types
  • Asynchronous Programming (async/await)
  • Null Safety
  • Data Handling and Serialization/Deserialization
    • JSON
    • Object-Relational Mapping (ORM) with Dapper
  • Error Handling and Logging
  • Dependency Injection (DI)

• Develop REST API with .NET C#

  • Create project with Web API
  • Basic project structure
    • Controller
    • Service/Business
    • Repository
  • RESTful Design
    • Resource-Oriented Design
    • HTTP Methods
    • HTTP Status Codes
    • Input Validation
  • Properties of REST API
    • API documentation with Swagger
    • API Testing
      • xunit
      • postman and newman
    • Observability service
      • Logging with NLog
      • Metric with prometheus
      • Tracing with OpenTelemetry and Jaeger
  • Working with Database
    • ORM with Dapper
    • Working with SQLite

3. การพัฒนา semantic search ด้วย Vector Database

• 2 days

Software Requirements

• Python 3.13
• VS Code
• Jupyter Notebook or Google Colab
• Vector database
  • quadrant
• Ollama local

Outline

• Day 1: Introduction to Semantic Search and Vector Databases
  • Overview of Semantic Search
    • Key-word search
    • Semantic Search
    • Hybrid Search
    • Use Cases in each type of search
  • Introduction to vector datatypes and embeddings
    • What are vectors?
    • How to convert data into vectors (embeddings)
    • Popular embedding models and libraries
    • Choosing the right embedding model for your data
    • Practical session: Generating embeddings from sample data
  • Understanding Vector Databases
  • Setting up the Environment
    • Ingesting Data into a Vector Database
    • Exploring Vector Search Capabilities
    • Hands-on Exercises
• Day 2: Building a Semantic Search Application
  • Integrating Vector Database with Application
  • Implementing Semantic Search Functionality
  • Testing and Optimization Techniques
  • Workshop: Building a Simple Semantic Search Application
  • Q&A and Wrap-up