Tags¶
Browse labs by tag. Click any tag to see all related labs.
L300¶
- Lab 038: AI Cost Optimization
- Lab 039: Vector Database Comparison
- Lab 043: Multimodal Agents with GPT-4o Vision
a2a¶
accessibility¶
adaptive-cards¶
ag-ui¶
agent¶
agent-framework¶
agent-governance¶
agent-loop¶
agent-service¶
agentic-ai¶
agentic-rag¶
agents¶
- Lab 029: LangChain & LangGraph Basics
- Lab 086: Customize Copilot
- Lab 098: Context Engineering for Agents
- Lab 107: How GitHub Copilot Works
ai-controls¶
ai-credits¶
ai-functions¶
analytics¶
anthropic¶
apim¶
architecture¶
attention¶
autofix¶
autogen¶
automation¶
awareness¶
azure¶
- Lab 009: Azure OpenAI Service Quickstart
- Lab 028: Deploy MCP to Azure Container Apps
- Lab 030: Foundry Agent Service + MCP
- Lab 031: pgvector Semantic Search on Azure
- Lab 038: AI Cost Optimization
- Lab 039: Vector Database Comparison
azure-monitor¶
azure-openai¶
azure-required¶
- Lab 009: Azure OpenAI Service Quickstart
- Lab 028: Deploy MCP to Azure Container Apps
- Lab 030: Foundry Agent Service + MCP
- Lab 031: pgvector Semantic Search on Azure
batch¶
batch-enrichment¶
beginner¶
- Lab 001: What are AI Agents?
- Lab 002: AI Agent Landscape
- Lab 003: Choosing the Right Tool
- Lab 006: What is RAG?
- Lab 008: Responsible AI for Agents
benchmark¶
benchmarking¶
bicep¶
browser-automation¶
cache¶
capstone¶
chain-of-thought¶
challenge¶
- Défi 001 : Construire une boucle d'agent à partir de zéro
- Défi 002 : Corriger un pipeline RAG cassé
- Défi 003 : Défendre un agent contre le prompt injection
- Défi 004 : Optimiser un budget de tokens
- Défi 005 : Construire un outil sûr de style MCP
- Défi 006 : Investiguer un incident d'observabilité d'agent
- Défi 007 : Red team des garde-fous d'agents
- Défi 008 : Construire un agent 100 % local
- Défi 009 : Réussir uniquement avec Copilot Free
- Défi 010 : De la spécification à la pull request
- Défi 011 : RAG guidé par l'évaluation
- Défi 012 : Durcir un serveur MCP
chat-participant¶
ci¶
cicd¶
citations¶
claude-code¶
cloud-agent¶
code-review¶
coding-agents¶
coding-tools¶
comparison¶
compliance¶
computer-use¶
connectors¶
container-apps¶
content-safety¶
context¶
context-engineering¶
context-window¶
copilot¶
copilot-analytics¶
copilot-cli¶
copilot-free¶
copilot-sdk¶
copilot-studio¶
copilotkit¶
cost¶
cost-optimization¶
csharp¶
cua¶
custom-instructions¶
data-agent¶
dax¶
declarative-agents¶
deep-research¶
deepseek-r1¶
desktop¶
developer-experience¶
devops¶
dlp¶
document-ingestion¶
dora¶
dotnet¶
dspm¶
edge-ai¶
embeddings¶
enterprise¶
- Lab 046: Microsoft Agent 365 — Enterprise Governance
- Lab 047: Work IQ — Copilot Analytics
- Lab 048: Work IQ — Impact Analytics & Power BI
- Lab 056: Federated Copilot Connectors + MCP
- Lab 063: Agent Identity — Entra OBO
- Lab 064: Securing MCP with APIM
- Lab 065: Purview DSPM for AI
- Lab 066: Copilot Studio Governance
- Lab 074: Foundry Agent Service
entra-id¶
etl¶
evaluation¶
- Défi 002 : Corriger un pipeline RAG cassé
- Lab 005: Prompting & Context Engineering
- Lab 022: RAG with GitHub Models + pgvector
- Lab 035: Agent Evaluation with Azure AI Eval SDK
- Lab 073: Agent Benchmarking — SWE-bench
events¶
eventstreams¶
fabric¶
- Lab 051: Fabric IQ — Real-Time Intelligence
- Lab 052: Fabric IQ — Conversational Data Agent
- Lab 053: Fabric IQ — AI Functions
- Lab 075: Power BI Copilot — Analytics
federation¶
finops¶
foundations¶
foundry¶
- Lab 009: Azure OpenAI Service Quickstart
- Lab 030: Foundry Agent Service + MCP
- Lab 042: Enterprise RAG with Evaluations
- Lab 049: Foundry IQ — Agent Tracing
- Lab 050: Multi-Agent Observability
- Lab 074: Foundry Agent Service
foundry-local¶
free¶
- Lab 001: What are AI Agents?
- Lab 002: AI Agent Landscape
- Lab 003: Choosing the Right Tool
- Lab 006: What is RAG?
- Lab 008: Responsible AI for Agents
- Lab 009: Azure OpenAI Service Quickstart
- Lab 010: GitHub Copilot First Steps
- Lab 015: Ollama — Local LLMs
- Lab 016: Copilot Agent Mode
- Lab 020: MCP Server in Python
- Lab 021: MCP Server in C#
- Lab 024: Teams AI Library Bot
- Lab 025: VS Code Copilot Chat Participant
- Lab 026: Agentic RAG Pattern
- Lab 027: Agent Memory Patterns
- Lab 029: LangChain & LangGraph Basics
- Lab 032: Row Level Security for Agents
- Lab 033: Agent Observability with App Insights
- Lab 035: Agent Evaluation with Azure AI Eval SDK
- Lab 036: Prompt Injection Defense & Security
- Lab 037: CI/CD for AI Agents
- Lab 044: Phi-4 + Ollama in Production
- Lab 054: A2A Protocol
- Lab 078: Foundry Local — Run Models Offline
free-trial¶
frontend¶
full-stack¶
function-calling¶
genai-conventions¶
gh-300¶
github-actions¶
github-copilot¶
- Défi 009 : Réussir uniquement avec Copilot Free
- Défi 010 : De la spécification à la pull request
- Défi 011 : RAG guidé par l'évaluation
- Lab 010: GitHub Copilot First Steps
- Lab 016: Copilot Agent Mode
- Lab 025: VS Code Copilot Chat Participant
- Lab 041 : Extension GitHub Copilot Personnalisée — Retiré
- Lab 045 : GitHub Copilot Workspace — Retiré
- Lab 085: Copilot Prompt & Context Engineering
- Lab 086: Customize Copilot
- Lab 087: Copilot + MCP
- Lab 088: Copilot CLI
- Lab 089: Spec-Driven Development with Spec Kit
- Lab 090: Testing & Legacy Modernization
- Lab 091: Copilot Cloud Agent & Code Review
- Lab 092: Secure Code with Copilot
- Lab 093: Copilot SDK
- Lab 094: Copilot Governance & FinOps
- Lab 095: Measuring Copilot Impact
- Lab 096: Capstone — Agentic DevOps
- Lab 107: How GitHub Copilot Works
github-mcp-server¶
github-models¶
- Lab 020: MCP Server in Python
- Lab 021: MCP Server in C#
- Lab 024: Teams AI Library Bot
- Lab 026: Agentic RAG Pattern
- Lab 027: Agent Memory Patterns
- Lab 029: LangChain & LangGraph Basics
- Lab 035: Agent Evaluation with Azure AI Eval SDK
- Lab 043: Multimodal Agents with GPT-4o Vision
golden-set¶
governance¶
- Lab 064: Securing MCP with APIM
- Lab 065: Purview DSPM for AI
- Lab 066: Copilot Studio Governance
- Lab 094: Copilot Governance & FinOps
gpt¶
gpt-4o¶
gpt4o-vision¶
graphrag¶
gratuit¶
guardrails¶
- Défi 007 : Red team des garde-fous d'agents
- Lab 082: Agent Guardrails — NeMo & Content Safety
- Lab 084: Capstone — OutdoorGear Agent
hallucination¶
hooks¶
identity¶
images¶
incident¶
interoperability¶
iot¶
jailbreak¶
javascript¶
json-schema¶
kit¶
- Lab 014: Hello Agent with Microsoft Agent Framework
- Lab 023: Tools & Memory in Microsoft Agent Framework
- Lab 034: Agent Framework Orchestration Patterns
- Lab 040: Production Multi-Agent with Agent Framework
- Lab 042: Enterprise RAG with Evaluations
- Lab 102: Fine-tuning vs RAG vs Prompting + LoRA
- Lab 103: Train a Mini-GPT on CPU
- Lab 104: MCP 2026-07-28
- Lab 105: MCP Apps
- Lab 106: Agent Framework Orchestration
knowledge-graph¶
kql¶
langchain¶
langgraph¶
legacy¶
llm¶
- Lab 004: How LLMs Work 2.0
- Lab 005: Prompting & Context Engineering
- Lab 009: Azure OpenAI Service Quickstart
- Lab 013: GitHub Models — Free LLMs
- Lab 097: Tokens, Sampling & Context Windows
llm-as-judge¶
llm-evaluation¶
local¶
- Défi 001 : Construire une boucle d'agent à partir de zéro
- Défi 002 : Corriger un pipeline RAG cassé
- Défi 003 : Défendre un agent contre le prompt injection
- Défi 004 : Optimiser un budget de tokens
- Défi 005 : Construire un outil sûr de style MCP
- Défi 006 : Investiguer un incident d'observabilité d'agent
- Défi 007 : Red team des garde-fous d'agents
- Lab 007: Local Embeddings & Similarity
- Lab 017: Reliable Structured Output
- Lab 018: Typed Tool Calling
- Lab 019: Safe Streaming Responses
- Lab 022: RAG with GitHub Models + pgvector
local-ai¶
local-first¶
- Lab 014: Hello Agent with Microsoft Agent Framework
- Lab 023: Tools & Memory in Microsoft Agent Framework
- Lab 034: Agent Framework Orchestration Patterns
- Lab 040: Production Multi-Agent with Agent Framework
- Lab 042: Enterprise RAG with Evaluations
- Lab 102: Fine-tuning vs RAG vs Prompting + LoRA
- Lab 103: Train a Mini-GPT on CPU
- Lab 104: MCP 2026-07-28
- Lab 105: MCP Apps
- Lab 106: Agent Framework Orchestration
local-inference¶
local-llm¶
local-validator¶
- Défi 010 : De la spécification à la pull request
- Défi 011 : RAG guidé par l'évaluation
- Défi 012 : Durcir un serveur MCP
lora¶
low-code¶
m365¶
m365-copilot¶
maf¶
- Lab 014: Hello Agent with Microsoft Agent Framework
- Lab 023: Tools & Memory in Microsoft Agent Framework
- Lab 034: Agent Framework Orchestration Patterns
- Lab 040: Production Multi-Agent with Agent Framework
- Lab 106: Agent Framework Orchestration
manifest¶
markitdown¶
mcp¶
- Défi 005 : Construire un outil sûr de style MCP
- Défi 012 : Durcir un serveur MCP
- Lab 020: MCP Server in Python
- Lab 021: MCP Server in C#
- Lab 028: Deploy MCP to Azure Container Apps
- Lab 030: Foundry Agent Service + MCP
- Lab 046: Microsoft Agent 365 — Enterprise Governance
- Lab 055: A2A + MCP Capstone
- Lab 056: Federated Copilot Connectors + MCP
- Lab 064: Securing MCP with APIM
- Lab 080: MarkItDown + MCP
- Lab 084: Capstone — OutdoorGear Agent
- Lab 087: Copilot + MCP
- Lab 088: Copilot CLI
- Lab 092: Secure Code with Copilot
- Lab 093: Copilot SDK
- Lab 104: MCP 2026-07-28
mcp-apps¶
memory¶
metrics¶
microsoft-365¶
migration¶
modeles-locaux¶
models¶
monitoring¶
multi-agent¶
- Lab 050: Multi-Agent Observability
- Lab 054: A2A Protocol
- Lab 055: A2A + MCP Capstone
- Lab 074: Foundry Agent Service
- Lab 079: Deep Research Agents
multimodal¶
- Lab 043: Multimodal Agents with GPT-4o Vision
- Lab 059: Voice Agents — Realtime API
- Lab 083: Multi-Modal RAG
nemo¶
nl-to-sql¶
no-account-needed¶
- Lab 001: What are AI Agents?
- Lab 002: AI Agent Landscape
- Lab 003: Choosing the Right Tool
- Lab 006: What is RAG?
- Lab 008: Responsible AI for Agents
no-code¶
npu¶
o3¶
oauth¶
obo¶
observability¶
- Défi 006 : Investiguer un incident d'observabilité d'agent
- Lab 033: Agent Observability with App Insights
- Lab 046: Microsoft Agent 365 — Enterprise Governance
- Lab 049: Foundry IQ — Agent Tracing
- Lab 050: Multi-Agent Observability
- Lab 084: Capstone — OutdoorGear Agent
- Lab 096: Capstone — Agentic DevOps
ollama¶
- Lab 013: GitHub Models — Free LLMs
- Lab 015: Ollama — Local LLMs
- Lab 044: Phi-4 + Ollama in Production
ollama-alternative¶
on-device¶
onnx¶
openai¶
opentelemetry¶
- Lab 033: Agent Observability with App Insights
- Lab 049: Foundry IQ — Agent Tracing
- Lab 050: Multi-Agent Observability
owasp¶
pandas¶
pdf¶
persona-admin¶
persona-analyst¶
persona-architect¶
persona-developer¶
persona-student¶
pgvector¶
phase-2¶
phase-3¶
- Défi 012 : Durcir un serveur MCP
- Lab 014: Hello Agent with Microsoft Agent Framework
- Lab 023: Tools & Memory in Microsoft Agent Framework
- Lab 034: Agent Framework Orchestration Patterns
- Lab 040: Production Multi-Agent with Agent Framework
- Lab 042: Enterprise RAG with Evaluations
- Lab 102: Fine-tuning vs RAG vs Prompting + LoRA
- Lab 103: Train a Mini-GPT on CPU
- Lab 104: MCP 2026-07-28
- Lab 105: MCP Apps
- Lab 106: Agent Framework Orchestration
phi-4¶
phi-silica¶
phi4¶
pii¶
playwright¶
postgresql¶
power-bi¶
power-platform¶
privacy¶
pro-code¶
proactive¶
production¶
- Lab 038: AI Cost Optimization
- Lab 039: Vector Database Comparison
- Lab 074: Foundry Agent Service
- Lab 084: Capstone — OutdoorGear Agent
prompt-engineering¶
prompt-injection¶
- Défi 003 : Défendre un agent contre le prompt injection
- Lab 036: Prompt Injection Defense & Security
prompting¶
protocol¶
purview¶
pydantic¶
python¶
- Défi 001 : Construire une boucle d'agent à partir de zéro
- Défi 002 : Corriger un pipeline RAG cassé
- Défi 003 : Défendre un agent contre le prompt injection
- Défi 004 : Optimiser un budget de tokens
- Défi 005 : Construire un outil sûr de style MCP
- Défi 006 : Investiguer un incident d'observabilité d'agent
- Défi 007 : Red team des garde-fous d'agents
- Défi 008 : Construire un agent 100 % local
- Lab 009: Azure OpenAI Service Quickstart
- Lab 013: GitHub Models — Free LLMs
- Lab 015: Ollama — Local LLMs
- Lab 020: MCP Server in Python
- Lab 026: Agentic RAG Pattern
- Lab 027: Agent Memory Patterns
- Lab 029: LangChain & LangGraph Basics
- Lab 032: Row Level Security for Agents
- Lab 033: Agent Observability with App Insights
- Lab 035: Agent Evaluation with Azure AI Eval SDK
- Lab 036: Prompt Injection Defense & Security
- Lab 037: CI/CD for AI Agents
- Lab 043: Multimodal Agents with GPT-4o Vision
- Lab 044: Phi-4 + Ollama in Production
- Lab 047: Work IQ — Copilot Analytics
- Lab 048: Work IQ — Impact Analytics & Power BI
- Lab 049: Foundry IQ — Agent Tracing
- Lab 050: Multi-Agent Observability
- Lab 051: Fabric IQ — Real-Time Intelligence
- Lab 052: Fabric IQ — Conversational Data Agent
- Lab 053: Fabric IQ — AI Functions
- Lab 054: A2A Protocol
- Lab 055: A2A + MCP Capstone
- Lab 057: Computer-Using Agents
- Lab 058: Browser Automation — OpenAI CUA
- Lab 059: Voice Agents — Realtime API
- Lab 060: Reasoning Models — o3 & DeepSeek R1
- Lab 061: SLMs — Phi-4 Mini
- Lab 067: GraphRAG — Knowledge Graphs
- Lab 070: Agent UX Patterns
- Lab 072: Structured Outputs Reliability Benchmark
- Lab 073: Agent Benchmarking — SWE-bench
- Lab 074: Foundry Agent Service
- Lab 076: Microsoft Agent Framework
- Lab 077: AG-UI Protocol
- Lab 078: Foundry Local — Run Models Offline
- Lab 079: Deep Research Agents
- Lab 080: MarkItDown + MCP
- Lab 083: Multi-Modal RAG
quiz¶
- Lab 014: Hello Agent with Microsoft Agent Framework
- Lab 023: Tools & Memory in Microsoft Agent Framework
- Lab 034: Agent Framework Orchestration Patterns
- Lab 040: Production Multi-Agent with Agent Framework
- Lab 042: Enterprise RAG with Evaluations
- Lab 102: Fine-tuning vs RAG vs Prompting + LoRA
- Lab 103: Train a Mini-GPT on CPU
- Lab 104: MCP 2026-07-28
- Lab 105: MCP Apps
- Lab 106: Agent Framework Orchestration
rag¶
- Défi 002 : Corriger un pipeline RAG cassé
- Défi 003 : Défendre un agent contre le prompt injection
- Défi 008 : Construire un agent 100 % local
- Lab 006: What is RAG?
- Lab 022: RAG with GitHub Models + pgvector
- Lab 026: Agentic RAG Pattern
- Lab 031: pgvector Semantic Search on Azure
- Lab 039: Vector Database Comparison
- Lab 067: GraphRAG — Knowledge Graphs
- Lab 083: Multi-Modal RAG
- Lab 084: Capstone — OutdoorGear Agent
real-time-intelligence¶
realtime-api¶
reasoning¶
red-team¶
refactoring¶
reliability¶
responsible-ai¶
retire¶
- Lab 041 : Extension GitHub Copilot Personnalisée — Retiré
- Lab 045 : GitHub Copilot Workspace — Retiré
retrieval¶
- Défi 002 : Corriger un pipeline RAG cassé
- Lab 007: Local Embeddings & Similarity
- Lab 022: RAG with GitHub Models + pgvector
- Lab 098: Context Engineering for Agents
roi¶
routing¶
safety¶
- Défi 007 : Red team des garde-fous d'agents
- Lab 057: Computer-Using Agents
- Lab 058: Browser Automation — OpenAI CUA
- Lab 082: Agent Guardrails — NeMo & Content Safety
sampling¶
schema¶
secret-scanning¶
security¶
- Défi 003 : Défendre un agent contre le prompt injection
- Défi 012 : Durcir un serveur MCP
- Lab 008: Responsible AI for Agents
- Lab 018: Typed Tool Calling
- Lab 032: Row Level Security for Agents
- Lab 036: Prompt Injection Defense & Security
- Lab 063: Agent Identity — Entra OBO
- Lab 064: Securing MCP with APIM
- Lab 092: Secure Code with Copilot
- Lab 100: OWASP Threat Modeling for LLM Apps
semantic-kernel¶
similarity¶
simulation¶
skills¶
slm¶
space¶
spec-driven¶
spec-driven-development¶
spec-kit¶
sqlite¶
sse¶
streaming¶
structured-output¶
structured-outputs¶
swe-bench¶
synthesis¶
tables¶
teams¶
- Lab 011: Copilot Studio First Agent
- Lab 024: Teams AI Library Bot
- Lab 069: Declarative Agents for M365 Copilot
- Lab 070: Agent UX Patterns
testing¶
threat-modeling¶
token-budget¶
tokens¶
tools¶
- Défi 001 : Construire une boucle d'agent à partir de zéro
- Défi 005 : Construire un outil sûr de style MCP
- Défi 008 : Construire un agent 100 % local
- Lab 018: Typed Tool Calling
- Lab 098: Context Engineering for Agents