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Home - Lessons - Microsoft Azure AI Engineer Associate Training - M9 — MLOps and Monitoring - Application Insights integration

Microsoft Azure AI Engineer Associate Training Application Insights integration

Microsoft Azure AI Engineer Associate Training

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M1 — Azure AI Fundamentals and AI-102 Scope

AI-102 skills measured

Azure AI Foundry and AI Studio

Identity and access for AI services

Responsible AI principles

M2 — Python and REST for AI Workloads

Python SDK patterns

Auth with keys and Microsoft Entra ID

Observability basics

M3 — Azure Cognitive Services – Vision

Image Analysis and Custom Vision

Form Recognizer / Document Intelligence

Face service overview

Vision Studio walkthrough

M4 — Speech and Language Services

Speech-to-text and text-to-speech

Translator and Language service

CLU (Conversational Language Understanding)

Question answering

M5 — Decision Services and Anomaly Detection

Anomaly Detector

Content Moderator

Metrics Advisor

M6 — Azure OpenAI Service

Model selection and quotas

Prompt engineering patterns

Function calling and tools

Safety and content filters

M7 — RAG with Azure Cognitive Search

Index design and skillsets

Vector search and hybrid retrieval

Prompt Flow orchestration

Grounding and citations

M8 — Azure Machine Learning

Workspaces and compute

Designer vs SDK

AutoML and pipelines

Endpoints and online inference

M9 — MLOps and Monitoring

Model registry and versioning

Data drift detection

A/B testing online endpoints

Application Insights integration

M10 — Bot Framework and Conversational AI

Bot Framework SDK

Adaptive dialogs and language generation

Bot analytics and telemetry

Handover to live agents

M11 — Security, Cost and Responsible AI

Virtual network integration

Customer-managed keys

Cost optimisation patterns

Bias, drift and content safety

M12 — Capstone and AI-102 Mocks

End-to-end GenAI application

Evaluation and monitoring

AI-102 mapped mocks

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