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AI-102T00 Develop AI solutions in Azure

„AI-102 Designing and Implementing an Azure AI Solution“ ist für Softwareentwickler gedacht, die KI-basierte Anwendungen entwickeln möchten, die Azure KI Services, Azure KI-Suche und Azure OpenAI nutzen. In diesem Kurs wird C# oder Python als Programmiersprache verwendet.
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Nehmen Sie an einem unserer Standorte Frankfurt, München und Wien oder virtuell an unseren Klassenraumtrainings teil. Unter “Termin buchen” werden Ihnen alle Optionen angezeigt, zuerst sortiert nach Standort, dann nach Datum.

Softwareentwickler, die am Kompilieren, Verwalten und Bereitstellen von KI-Lösungen interessiert sind, die Azure KI Services, Azure KI-Suche und Azure OpenAI nutzen. Sie sind mit C# oder Python vertraut und verfügen über Kenntnisse in der Verwendung von REST-basierten APIs zum Kompilieren von maschinellem Sehen, Sprachanalyse, Wissensbeschaffung, intelligenter Suche und generativen KI-Lösungen in Azure.
• Vertrautheit mit Azure und dem Azure-Portal
• Erfahrung mit der C#- oder Python-Programmierung

Wichtige Information

Dieses Training behandelt prüfungsrelevante Themen zum Examen: Microsoft Certified: Azure AI Engineer Associate

Was werden Sie in diesem Training erlernen?

• Überlegungen zur KI-gestützten Anwendungsentwicklung zu beschreiben
• Azure Cognitive Services erstellen, konfigurieren, einsetzen und sichern
• Anwendungen entwickeln, die Text analysieren
• Entwickeln von sprachgesteuerten Anwendungen
• Erstellen von Anwendungen mit Funktionen zum Verstehen natürlicher Sprache
• Erstellen von QnA-Anwendungen
• Erstellen von Konversationslösungen mit Bots
• Nutzung von Bildverarbeitungsdiensten zur Analyse von Bildern und Videos
• Erstellen benutzerdefinierter Computer-Vision-Modelle
• Entwicklung von Anwendungen zur Erkennung, Analyse und Erkennung von Gesichtern
• Entwicklung von Anwendungen, die Text in Bildern und Dokumenten lesen und verarbeiten
• Erstellung intelligenter Suchlösungen für die Wissenssuche

Agenda

Develop an AI app with the Azure AI Foundry SDK
What is the Azure AI Foundry SDK?
Work with project connections
Create a chat client
Create a generative AI chat app
Get started with prompt flow to develop language model apps in the Azure AI Foundry
Understand the development lifecycle of a large language model (LLM) app
Understand core components and explore flow types
Explore connections and runtimes
Explore variants and monitoring options
Get started with prompt flow
Develop a RAG-based solution with your own data using Azure AI Foundry
Understand how to ground your language model
Make your data searchable
Create a RAG-based client application
Implement RAG in a prompt flow
Create a generative AI app that uses your own data
Fine-tune a language model with Azure AI Foundry
Understand when to fine-tune a language model
Prepare your data to fine-tune a chat completion model
Explore fine-tuning language models in Azure AI Studio
Fine-tune a language model
Implement a responsible generative AI solution in Azure AI Foundry
Plan a responsible generative AI solution
Map potential harms
Measure potential harms
Mitigate potential harms
Manage a responsible generative AI solution
Apply content filters to prevent the output of harmful content
Evaluate generative AI performance in Azure AI Foundry portal
Assess the model performance
Manually evaluate the performance of a model
Automated evaluations
Evaluate generative AI model performance
Create a custom text classification solution
Understand types of classification projects
Understand how to build text classification projects
Classify text
Develop an AI agent with Azure AI Foundry Agent Service
What is an AI agent
How to use Azure AI Foundry Agent Service
Develop agents with the Azure AI Foundry Agent Service
Build an AI agent
Integrate custom tools into your agent
Why use custom tools
Options for implementing custom tools
How to integrate custom tools
Build an agent with custom tools
Develop an AI agent with Semantic Kernel
Understand Semantic Kernel AI agents
Create an Azure AI agent with Semantic Kernel
Add plugins to Azure AI agent
Develop an Azure AI agent with the Semantic Kernel SDK
Orchestrate a multi-agent solution using Semantic Kernel
Understand the Semantic Kernel Agent Framework
Create an agent group chat
Design an agent selection strategy
Define a chat termination strategy
Develop a multi-agent solution
Analyze text with Azure AI Language
Provision an Azure AI Language resource
Detect language
Extract key phrases
Analyze sentiment
Extract entities
Extract linked entities
Analyze text
Create question answering solutions with Azure AI Language
Understand question answering
Compare question answering to Azure AI Language understanding
Create a knowledge base
Implement multi-turn conversation
Test and publish a knowledge base
Use a knowledge base
Improve question answering performance
Create a question answering solution
Build a conversational language understanding model
Understand prebuilt capabilities of the Azure AI Language service
Understand resources for building a conversational language understanding model
Define intents, utterances, and entities
Use patterns to differentiate similar utterances
Use pre-built entity components
Train, test, publish, and review a conversational language understanding model
Build an Azure AI services conversational language understanding model
Choose and deploy models from the model catalog in Azure AI Foundry portal
Explore the model catalog
Deploy a model to an endpoint
Optimize model performance
Explore, deploy, and chat with language models
Get started with AI agent development on Azure
What are AI agents?
Options for agent development
Azure AI Foundry Agent Service
Explore AI Agent development
Custom named entity recognition
Understand custom named entity recognition
Label your data
Train and evaluate your model
Extract custom entities
Create speech-enabled apps with Azure AI services
Provision an Azure resource for speech
Use the Azure AI Speech to Text API
Use the text to speech API
Configure audio format and voices
Use Speech Synthesis Markup Language
Create a speech-enabled app
Create a knowledge mining solution with Azure AI Search
What is Azure AI Search?
Extract data with an indexer
Enrich extracted data with AI skills
Search an index
Persist extracted information in a knowledge store
Create a knowledge mining solution
Extract data from forms with Azure Document intelligence
What is Azure Document Intelligence?
Get started with Azure Document Intelligence
Train custom models
Use Azure Document Intelligence models
Use the Azure Document Intelligence Studio
Extract data from custom forms
Use prebuilt Document intelligence models
Understand prebuilt models
Use the General Document, Read, and Layout models
Use financial, ID, and tax models
Analyze a document using Azure AI Document Intelligence
Create an Azure AI Content Understanding client application
Prepare to use the AI Content Understanding REST API
Create a Content Understanding analyzer
Analyze content
Develop a Content Understanding client application
Create a multimodal analysis solution with Azure AI Content Understanding
What is Azure AI Content Understanding?
Create a Content Understanding analyzer
Use the Content Understanding REST API
Extract information from multimodal content
Generate images with AI
What are image-generation models?
Explore image-generation models in Azure AI Foundry portal
Create a client application that uses an image generation model
Generate images with AI
Develop a vision-enabled generative AI application
Deploy a multimodal model
Develop a vision-based chat app
Develop a vision-enabled chat app
Analyze video
Understand Azure Video Indexer capabilities
Extract custom insights
Use Video Analyzer widgets and APIs
Analyze video
Detect objects in images
Use Azure AI Custom Vision for object detection
Train an object detector
Develop an object detection client application
Detect objects in images
Classify images
Azure AI Custom Vision
Train an image classification model
Create an image classification client application
Classify images
Detect, analyze, and recognize faces
Plan a face detection, analysis, or recognition solution
Detect and analyze faces
Verify and identify faces
Responsible AI considerations for face-based solutions
Detect and analyze faces
Read text in images
Explore Azure AI options for reading text
Read text with Azure AI Vision Image Analysis
Read text in images
Analyze images
Provision an Azure AI Vision resource
Analyze an image
Analyze images
Develop an audio-enabled generative AI application
Deploy a multimodal model
Develop an audio-based chat app
Develop an audio-enabled chat app
Translate speech with the Azure AI Speech service
Provision an Azure resource for speech translation
Translate speech to text
Synthesize translations
Translate speech
Translate text with Azure AI Translator service
Provision an Azure AI Translator resource
Understand language detection, translation, and transliteration
Specify translation options
Define custom translations
Translate text with the Azure AI Translator service
Plan and prepare to develop AI solutions on Azure
What is AI?
Azure AI services
Azure AI Foundry
Developer tools and SDKs
Responsible AI
Prepare for an AI development project
  • Group Exercise: Review Architecting on AWS core best practices
  • Lab 1: Securing Amazon S3 VPC Endpoint Communications
  • Group Exercise: Review Architecting on AWS core best practices
  • AWS Organizations for multi-account access and permissions
  • AWS SSO to simplify access and authentication across AWS accounts and third-party services
  • AWS Control Tower
  • Permissions, access, and authentication
  • Group Exercise: Review Architecting on AWS core best practices
  • Lab 1: Securing Amazon S3 VPC Endpoint Communications
  • Group Exercise: Review Architecting on AWS core best practices
  • Lab 1: Securing Amazon S3 VPC Endpoint Communications

Dein Training im Überblick

Dauer 5 Tage
Trainingssprache Deutsch
Trainingsart brainyCLASS (offen)

2.980,00 €

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