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AI Learning Path

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AI Foundations & Core Machine Learning
Fundamental AI Concepts
Fundamentals of Machine Learning
Introduction to Azure AI Services
Computer Vision
Nlp
Azure NLP Services
Fundamentals of Azure OpenAI
generative AI
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Fundamentals of AI and ML
Fundamentals of Generative AI
Applications of Foundation Models
Guidelines for Responsible AI
Security
Compliance, and Governance for AI Solutions
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Getting Started with PyTorch
Working with Data
Building and Training Models
Model Deployment and Inference
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AI-Assisted Development & Coding Tools
Introduction to AI-Assisted Development
Planning Phase
Development Phase - Backend
Development Phase - Frontend
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AI pair programming
Copilot's Strengths and Limitations
Configuring Copilot Visual Studio
Comment-driven Development
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GitHub Copilot Basics
Prompt Engineering with Copilot
Advanced Features of Copilot
Management of GitHub Copilot
Using Copilot Efficiently (Project)
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Introduction to Cursor
Mastering Autocompletion in Cursor
Interacting with your Codebase
Inline Editing and Debugging in Cursor
Terminal Productivity
Customizing Cursor
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Getting Started with Claude Code
Working with Claude Code
Code Review with Claude Code
Security Auditing with Claude Code
Terminal Productivity
Advanced Features
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Introduction to Cline
Core Workflows - Prompt Engineering
Resources & Next Steps
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Ansible Playbook Basics
VS Code Extension
Integrating Cursor
Lightspeed
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Linux AI Tutor
Kubernetes AI Tutor
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Generative AI, LLMs & Prompt Engineering
Detailing for Clarity
Persona Play
Delimiters
Step-by-Step Clarification
Illustrating Through Examples
Specifying Output Length
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Text generator
Advanced usage techniques
Content moderation
Image generation and captioning
Fine-tuning techniques
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Introduction to AI
Foundation Models
Exploring ChatGPT
Understanding Prompt Engineering
Tokens and API Parameters
Implementing Word Completion
Implementing Code Completion
Building an Interactive Chatbot
Generating Images
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Evolution of AI Models
Retrieval-Augmented Generation (RAG)
AI Application(LLM) on Azure
Large Language Models(LLM)
Prompting Techniques in LLM
Application and Assessment of LLMs
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Running Your First Model
Essential Ollama CLI Commands
Ollama REST API Endpoints 
OpenAI Compatibility for Ollama
Customizing Models
Uploading Custom Models
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Core Machine Learning and AI Knowledge
Data Analysis
Experimentation
Software Development
Trustworthy AI
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Context Engineering (RAG, Vector Databases & MCP)
RAG Architecture Deep Dive
Document Processing and Chunking
Keyword Search & Retrieval
Semantic Search & Embeddings
Vector Databases
Building the RAG Pipeline
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Vector Database Foundations
From Data to Vectors: The Embedding Layer
Vector Similarity Explained
Building Vector Storage on AWS S3
Vector Database Landscape
Vector Database Internals
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mcp Introduction
Core Concepts
Leveraging MCP for Daily Work
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Overview of LangChain
Key Components of LangChain
Interacting with LLMs
Tips, Tricks, and Resources
introduction to LCEL
Adding Memory to LLM Apps
Performing Retrieval
implementing Chains
Using Tools
Building Agents
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Autonomous AI Agents & Advanced Workflows
LLMs work in real time
Embeddings & Vector Representations
LangChain
Vector Databases Deep Dive
Build Semantic Search Engine
Model Context Protocol
Advanced MCP Concepts
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Agent Architecture & Multi-Agent Systems
Building AI Agents
API Integrations & Tools
Practical Projects
Advanced Agents Projects
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Foundational Graph Concepts and Toolkit Setup
The Core Workflow: Nodes, Edges, and Routing
State Management and Iterative Loops
Context Management and Short-Term Memory
Long-Term Memory and Stateful Persistence
Human-in-the-Loop (HILT) Architectures
Advanced Control and Debugging UX
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Context Engineering (RAG, Vector Databases & MCP)
RAG Architecture Deep Dive
Document Processing and Chunking
Keyword Search & Retrieval
Semantic Search & Embeddings
Vector Databases
Building the RAG Pipeline
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Vector Database Foundations
From Data to Vectors: The Embedding Layer
Vector Similarity Explained
Building Vector Storage on AWS S3
Vector Database Landscape
Vector Database Internals
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mcp Introduction
Core Concepts
Leveraging MCP for Daily Work
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Overview of LangChain
Key Components of LangChain
Interacting with LLMs
Tips, Tricks, and Resources
introduction to LCEL
Adding Memory to LLM Apps
Performing Retrieval
implementing Chains
Using Tools
Building Agents
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FAQs

What is CI/CD?

CI/CD stands for Continuous Integration and Continuous Deployment. It is a set of practices and tools that enable developers to automatically build, test, and deploy software applications in a consistent and streamlined manner.

What is the difference between Continuous Integration and Continuous Deployment?

Continuous Integration (CI) focuses on automating the process of merging code changes from multiple developers into a shared repository and running automated tests to detect integration issues. Continuous Deployment (CD) takes CI a step further by automating the deployment of successfully tested code changes to production environments.

What are the benefits of implementing CI/CD?

CI/CD brings several benefits, including increased development speed, faster time to market, improved code quality, early bug detection, reduced manual errors, and easier collaboration among development teams.

What are some popular CI/CD tools?

Some popular CI/CD tools include Jenkins, GitLab CI/CD, CircleCI, Travis CI, TeamCity, and GitHub Actions. These tools provide automation capabilities for building, testing, and deploying applications.

How does CI/CD integrate with version control systems?

CI/CD tools integrate with version control systems, such as Git, to automatically trigger build and deployment processes whenever changes are pushed to the repository. This ensures that code changes are continuously tested and deployed.

What is the role of automated testing in CI/CD?

Automated testing is a crucial component of CI/CD. It includes unit tests, integration tests, and other types of automated tests that are executed as part of the CI/CD pipeline. These tests ensure the quality and reliability of the software before it is deployed.

Can CI/CD be used with containerized applications?

Yes, CI/CD is well-suited for containerized applications. Containers provide a consistent and reproducible environment for building, testing, and deploying applications, making it easier to integrate CI/CD pipelines into container-based workflows.

What are some common challenges in implementing CI/CD?

Some common challenges include managing complex deployment pipelines, ensuring compatibility across different environments, handling database migrations, maintaining good test coverage, and managing secrets and configurations securely.

Is CI/CD applicable only to cloud-based applications?

No, CI/CD can be applied to applications deployed in various environments, including on-premises, hybrid cloud, and multi-cloud environments. The principles of CI/CD can be adapted to different deployment scenarios.

What skills are important for implementing CI/CD?

Skills in version control systems (e.g., Git), scripting and automation (e.g., Bash, Python), knowledge of CI/CD tools, containerization (e.g., Docker), and infrastructure-as-code (e.g., Terraform) are valuable for successful implementation of CI/CD.