Description
While attending this 5 day camp – students will take two exams (AI-900 & AI-102) to achieve the Microsoft Certified: Azure AI Fundamentals w/ MCA Azure AI Engineer Associate certifications. This hands on, instructor led live camp teaches the knowledge needed for Azure AI Engineer job roles in addition to the knowledge needed for the certification exams (administered while attending).
The Microsoft Azure AI Fundamentals w/ Microsoft Azure AI Engineer Associate Certification Boot Camp is taught using Microsoft Official Courseware
AI-900T00: Microsoft Azure AI Fundaments
AI-102T00: Designing and Implementing an Azure AI Solution
Topics Covered in this Official Boot Camp:
Prepare to develop AI solutions on Azure
Define artificial intelligence
Understand AI-related terms
Understand considerations for AI Engineers
Understand considerations for responsible AI
Understand capabilities of Azure Machine Learning
Understand capabilities of Azure AI Services
Understand capabilities of Azure OpenAI Service
Understand capabilities of Azure AI Search
Define artificial intelligence
Understand AI-related terms
Understand considerations for AI Engineers
Understand considerations for responsible AI
Understand capabilities of Azure Machine Learning
Understand capabilities of Azure AI Services
Understand capabilities of the Azure OpenAI Service
Understand capabilities of Azure Cognitive Search
Create and consume Azure AI services
Create Azure AI services resources in an Azure subscription.
Identify endpoints, keys, and locations required to consume an Azure AI services resource.
Use a REST API and an SDK to consume Azure AI services.
Provision an Azure AI services resource
Identify endpoints and keys
Use a REST API
Use an SDK
Secure Azure AI services
Consider authentication for Azure AI services
Manage network security for Azure AI services
Consider authentication
Implement network security
Monitor Azure AI services
Monitor Azure AI services costs.
Create alerts and view metrics for Azure AI services.
Manage Azure AI services diagnostic logging.
Monitor cost
Create alerts
View metrics
Manage diagnostic logging
Deploy Azure AI services in containers
Create containers for reuse
Deploy to a container and secure a container
Consume Azure AI services from a container
Understand containers
Use Azure AI services containers
Analyze images
Provision an Azure AI Vision resource
Analyze an image
Generate a smart-cropped thumbnail
Provision an Azure AI Vision resource
Analyze an image
Generate a smart-cropped thumbnail and remove background
Image classification with custom Azure AI Vision models
Create a custom Azure AI Vision classification model
Understand image classification
Understand object detection
Train an image classifier in Vision Studio
Understand custom model types
Create a custom project
Label and train a custom model
Detect, analyze, and recognize faces
Identify options for face detection, analysis, and identification.
Understand considerations for face analysis.
Detect faces with the Computer Vision service.
Understand capabilities of the Face service.
Compare and match detected faces.
Implement facial recognition.
Identify options for face detection analysis and identification
Understand considerations for face analysis
Detect faces with the Azure AI Vision service
Understand capabilities of the face service
Compare and match detected faces
Implement facial recognition
Read Text in images and documents with Azure AI Vision Service
Read text from images using OCR
Use the Azure AI Vision service Image Analysis with SDKs and the REST API
Develop an application that can read printed and handwritten text
Explore Azure AI Vision options for reading text
Use the Read API
Analyze video
Describe Azure Video Indexer capabilities
Extract custom insights
Use Azure Video Indexer widgets and APIs
Understand Azure Video Indexer capabilities
Extract custom insights
Use Video Analyzer widgets and APIs
Analyze text with Azure AI Language
Detect language from text
Analyze text sentiment
Extract key phrases, entities, and linked entities
Provision an Azure AI Language resource
Detect language
Extract key phrases
Analyze sentiment
Extract entities
Extract linked entities
Create question answering solutions with Azure AI Language
Understand question answering and how it compares to language understanding.
Create, test, publish, and consume a knowledge base.
Implement multi-turn conversation and active learning.
Create a question answering bot to interact with using natural 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
Build a conversational language understanding model
Provision Azure resources for Azure AI Language resource
Define intents, utterances, and entities
Use patterns to differentiate similar utterances
Use pre-built entity components
Train, test, publish, and review an Azure AI Language 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
Create a custom text classification solution
Understand types of classification projects
Build a custom text classification project
Tag data, train, and deploy a model
Submit classification tasks from your own app
Understand types of classification projects
Understand how to build text classification projects
Custom named entity recognition
Understand tagging entities in extraction projects
Understand how to build entity recognition projects
Understand custom named entity recognition
Label your data
Train and evaluate your model
Translate text with Azure AI Translator service
Provision a Translator resource
Understand language detection, translation, and transliteration
Specify translation options
Define custom translations
Provision an Azure AI Translator resource
Understand language detection, translation, and transliteration
Specify translation options
Define custom translations
Create speech-enabled apps with Azure AI services
Provision an Azure resource for the Azure AI Speech service
Use the Azure AI Speech to text API to implement speech recognition
Use the Text to speech API to implement speech synthesis
Configure audio format and voices
Use Speech Synthesis Markup Language (SSML)
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
Translate speech with the Azure AI Speech service
Provision Azure resources for speech translation.
Generate text translation from speech.
Synthesize spoken translations.
Provision an Azure resource for speech translation
Translate speech to text
Synthesize translations
Create an Azure AI Search solution
Create an Azure AI Search solution
Develop a search application
Manage capacity
Understand search components
Understand the indexing process
Search an index
Apply filtering and sorting
Enhance the index
Create a custom skill for Azure AI Search
Implement a custom skill for Azure AI Search
Integrate a custom skill into an Azure AI Search skillset
Create a custom skill
Add a custom skill to a skillset
Create a knowledge store with Azure AI Search
Create a knowledge store from an Azure AI Search pipeline
View data in projections in a knowledge store
Define projections
Define a knowledge store
Enrich your data with Azure AI Language
Use Azure AI Language to enrich Azure AI Search indexes.
Enrich an AI Search index with custom classes.
Explore the available features of Azure AI Language
Enrich a search index in Azure AI Search with custom classes and Azure AI Language
Implement advanced search features in Azure AI Search
Improve the ranking of a document with term boosting
Improve the relevance of results by adding scoring profiles
Improve an index with analyzers and tokenized terms
Enhance an index to include multiple languages
Improve search experience by ordering results by distance from a given reference point
Improve the ranking of a document with term boosting
Improve the relevance of results by adding scoring profiles
Improve an index with analyzers and tokenized terms
Enhance an index to include multiple languages
Improve search experience by ordering results by distance from a given reference point
Build an Azure Machine Learning custom skill for Azure AI Search
Understand how to use a custom Azure Machine Learning skillset.
Use Azure Machine Learning to enrich Azure AI Search indexes.
Understand how to use a custom Azure Machine Learning skillset
Enrich a search index using an Azure Machine Learning model
Search data outside the Azure platform in Azure AI Search using Azure Data Factory
Use Azure Data Factory to copy data into an Azure AI Search Index
Use the Azure AI Search push API to add to an index from any external data source
Index data from external data sources using Azure Data Factory
Index any data using the Azure AI Search push API
Maintain an Azure AI Search solution
Use Language Studio to enrich Azure AI Search indexes
Enrich an AI Search index with custom classes
Manage security of an Azure AI Search solution
Optimize performance of an Azure AI Search solution
Manage costs of an Azure AI Search solution
Improve reliability of an Azure AI Search solution
Monitor an Azure AI Search solution
Debug search issues using the Azure portal
Perform search re-ranking with semantic ranking in Azure AI Search
Describe semantic ranking
Set up semantic ranking
Perform semantic ranking on an index
What is semantic ranking?
Set up semantic ranking
Perform vector search and retrieval in Azure AI Search
Describe vector search
Describe embeddings
Run vector search queries using the REST API
What is vector search?
Prepare your search
Understand embedding
Plan an Azure AI Document Intelligence solution
Describe the components of an Azure AI Document Intelligence solution.
Create and connect to Azure AI Document Intelligence resources in Azure.
Choose whether to use a prebuilt, custom, or composed model.
Understand AI Document Intelligence
Plan Azure AI Document Intelligence resources
Choose a model type
Use prebuilt Document intelligence models
Identify business problems that you can solve by using prebuilt models in Forms Analyzer.
Analyze forms by using the General Document, Read, and Layout models.
Analyze forms by using financial, ID, and tax prebuilt models.
Understand prebuilt models
Use the General Document, Read, and Layout models
Use financial, ID, and tax models
Extract data from forms with Azure Document intelligence
Identify how Document intelligence’s layout service, prebuilt models, and custom models can automate processes.
Use Document intelligence’s capabilities with SDKs, REST API, and Document Intelligence Studio.
Develop and test custom models.
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
Create a composed Document intelligence model
Describe business problems that you would use custom models and composed models to solve.
Train a custom model to obtain data from forms with unusual structures.
Create a composed model that can analyze forms in multiple formats.
Understand composed models
Assemble composed models
Build a Document intelligence custom skill for Azure AI search
Describe how a custom skill can enrich content passed through an Azure AI Search pipeline.
Build a custom skill that calls an Azure Forms Analyzer solution to obtain data from forms.
Understand Azure AI Search enrichment pipelines
Build an Azure AI Document Intelligence custom skill
Get started with Azure OpenAI Service
Create an Azure OpenAI Service resource and understand types of Azure OpenAI base models.
Use the Azure OpenAI Studio, console, or REST API to deploy a base model and test it in the Studio’s playgrounds.
Generate completions to prompts and begin to manage model parameters.
Access Azure OpenAI Service
Use Azure OpenAI Studio
Explore types of generative AI models
Deploy generative AI models
Use prompts to get completions from models
Test models in Azure OpenAI Studio’s playgrounds
Build natural language solutions with Azure Open AI Service
Integrate Azure OpenAI into your application
Differentiate between different endpoints available to your application
Generate completions to prompts using the REST API and language specific SDKs
Integrate Azure OpenAI into your app
Use Azure OpenAI REST API
Use Azure OpenAI SDK
Apply prompt engineering with Azure OpenAI Service
Understand the concept of prompt engineering and its role in optimizing Azure OpenAI models’ performance.
Know how to design and optimize prompts to better utilize AI models.
Include clear instructions, request output composition, and use contextual content to improve the quality of the model’s responses.
Understand prompt engineering
Write more effective prompts
Provide context to improve accuracy
Generate code with Azure OpenAI Service
Use natural language prompts to write code
Build unit tests and understand complex code with AI models
Generate comments and documentation for existing code
Construct code from natural language
Complete code and assist the development process
Fix bugs and improve your code
Generate images with Azure OpenAI Service
Describe the capabilities of DALL-E in the Azure openAI service
Use the DALL-E playground in Azure OpenAI Studio
Use the Azure OpenAI REST interface to integrate DALL-E image generation into your apps
What is DALL-E?
Explore DALL-E in Azure OpenAI Studio
Use the Azure OpenAI REST API to consume DALL-E models
Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service
Describe the capabilities of Azure OpenAI on your data
Configure Azure OpenAI to use your own data
Use Azure OpenAI API to generate responses based on your own data
Understand Retrieval Augmented Generation (RAG) with Azure OpenAI Service
Add your own data source
Chat with your model using your own data
Fundamentals of Responsible Generative AI
Describe an overall process for responsible generative AI solution development
Identify and prioritize potential harms relevant to a generative AI solution
Measure the presence of harms in a generative AI solution
Mitigate harms in a generative AI solution
Prepare to deploy and operate a generative AI solution responsibly
Plan a responsible generative AI solution
Identify potential harms
Measure potential harms
Mitigate potential harms
Operate a responsible generative AI solution