Designing and implementing a data science solution on Azure

Outline

• Explore Azure Machine Learning workspace resources and assets
  • Create an Azure Machine Learning workspace
  • Identify Azure Machine Learning resources
  • Identify Azure Machine Learning assets
  • Train models in the workspace
  • Exercise - Explore the workspace
  • Module assessment
• Explore developer tools for workspace interaction
  • Explore the studio
  • Explore the Python SDK
  • Explore the CLI
  • Exercise - Explore the developer tools
  • Module assessment
• Make data available in Azure Machine Learning
  • Understand URIs
  • Create a datastore
  • Create a data asset
  • Exercise - Make data available in Azure Machine Learning
  • Module assessment
• Work with compute targets in Azure Machine Learning
  • Choose the appropriate compute target
  • Create and use a compute instance
  • Create and use a compute cluster
  • Exercise - Work with compute resources
  • Module assessment
• Work with environments in Azure Machine Learning
  • Understand environments
  • Explore and use curated environments
  • Create and use custom environments
  • Exercise - Work with environments
  • Module assessment
• Find the best classification model with Automated Machine Learning
  • Preprocess data and configure featurization
  • Run an Automated Machine Learning experiment
  • Evaluate and compare models
  • Exercise - Find the best classification model with Automated Machine Learning
  • Module assessment
• Track model training in Jupyter notebooks with MLflow
  • Configure MLflow for model tracking in notebooks
  • Train and track models in notebooks
  • Exercise - Track model training
  • Module assessment
• Run a training script as a command job in Azure Machine Learning
  • Convert a notebook to a script
  • Run a script as a command job
  • Use parameters in a command job
  • Exercise - Run a training script as a command job
  • Module assessment
• Track model training with MLflow in jobs
  • Track metrics with MLflow
  • View metrics and evaluate models
  • Exercise - Use MLflow to track training jobs
  • Module assessment
• Perform hyperparameter tuning with Azure Machine Learning
  • Define a search space
  • Configure a sampling method
  • Configure early termination
  • Use a sweep job for hyperparameter tuning
  • Exercise - Run a sweep job
  • Module assessment
• Run pipelines in Azure Machine Learning
  • Create components
  • Create a pipeline
  • Run a pipeline job
  • Exercise - Run a pipeline job
  • Module assessment
• Register an MLflow model in Azure Machine Learning
  • Log models with MLflow
  • Understand the MLflow model format
  • Register an MLflow model
  • Exercise - Log and register models with MLflow
  • Module assessment
• Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
  • Understand Responsible AI
  • Create the Responsible AI dashboard
  • Evaluate the Responsible AI dashboard
  • Exercise - Explore the Responsible AI dashboard
  • Module assessment
• Deploy a model to a managed online endpoint
  • Explore managed online endpoints
  • Deploy your MLflow model to a managed online endpoint
  • Deploy a model to a managed online endpoint
  • Test managed online endpoints
  • Exercise - Deploy an MLflow model to an online endpoint
  • Module assessment
• Deploy a model to a batch endpoint
  • Understand and create batch endpoints
  • Deploy your MLflow model to a batch endpoint
  • Deploy a custom model to a batch endpoint
  • Invoke and troubleshoot batch endpoints
  • Exercise - Deploy an MLflow model to a batch endpoint
  • Module assessment
• Plan and prepare to develop AI solutions on Azure
  • What is AI?
  • Azure AI services
  • Azure AI Foundry
  • Developer tools and SDKs
  • Responsible AI
  • Exercise - Prepare for an AI development project
  • Module assessment
• 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
  • Exercise - Explore, deploy, and chat with language models
  • Module assessment
• 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
  • Exercise - Create a generative AI chat app
  • Module assessment
• 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
  • Exercise - Get started with prompt flow
  • Module assessment
• 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
  • Exercise - Create a generative AI app that uses your own data
  • Module assessment
• 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
  • Exercise - Fine-tune a language model
  • Module assessment
• 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
  • Exercise - Apply content filters to prevent the output of harmful content
  • Module assessment
• Evaluate generative AI performance in Azure AI Foundry portal
  • Assess the model performance
  • Manually evaluate the performance of a model
  • Automated evaluations
  • Assess the performance of your generative AI apps
  • Exercise - Evaluate generative AI model performance
  • Module assessment