Microsoft Azure Data Scientist Associate

Section 1: Explore Azure Machine Learning workspace resources and assets

• Introduction
• 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
• Summary

Section 2: Explore developer tools for workspace interaction

• Introduction
• Explore the studio
• Explore the Python SDK
• Explore the CLI
• Exercise - Explore the developer tools
• Module assessment
• Summary

Section 3: Make data available in Azure Machine Learning

• Introduction
• Understand URIs
• Create a datastore
• Create a data asset
• Exercise - Make data available in Azure Machine Learning
• Module assessment
• Summary

Section 4: Work with compute targets in Azure Machine Learning

• Introduction
• Choose the appropriate compute target
• Create and use a compute instance
• Create and use a compute cluster
• Exercise - Work with compute resources
• Module assessment
• Summary

Section 5: Work with environments in Azure Machine Learning

• Introduction
• Understand environments
• Explore and use curated environments
• Create and use custom environments
• Exercise - Work with environments
• Module assessment
• Summary

Section 1: Find the best classification model with Automated Machine Learning

• Introduction
• 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
• Summary

Section 2: Track model training in Jupyter notebooks with MLflow

• Introduction
• Configure MLflow for model tracking in notebooks
• Train and track models in notebooks
• Exercise - Track model training
• Module assessment
• Summary

Section 1: Run a training script as a command job in Azure Machine Learning

• Introduction
• 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
• Summary

Section 2: Track model training with MLflow in jobs

• Introduction
• Track metrics with MLflow
• View metrics and evaluate models
• Exercise - Use MLflow to track training jobs
• Module assessment
• Summary

Section 3: Perform hyperparameter tuning with Azure Machine Learning

• Introduction
• 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
• Summary

Section 4: Run pipelines in Azure Machine Learning

• Introduction
• Create components
• Create a pipeline
• Run a pipeline job
• Exercise - Run a pipeline job
• Module assessment
• Summary

Section 1: Register an MLflow model in Azure Machine Learning

• Introduction
• Log models with MLflow
• Understand the MLflow model format
• Register an MLflow model
• Exercise - Log and register models with MLflow
• Module assessment
• Summary

Section 2: Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

• Introduction
• Understand Responsible AI
• Create the Responsible AI dashboard
• Evaluate the Responsible AI dashboard
• Exercise - Explore the Responsible AI dashboard
• Module assessment
• Summary

Section 1: Deploy a model to a managed online endpoint

• Introduction
• 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
• Summary

Section 2: Deploy a model to a batch endpoint

• Introduction
• 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
• Summary

Section 1: Plan and prepare to develop AI solutions on Azure

• Introduction
• What is AI?
• Azure AI services
• Azure AI Foundry
• Developer tools and SDKs
• Responsible AI
• Exercise - Prepare for an AI development project
• Module assessment
• Summary

Section 2: Choose and deploy models from the model catalog in Azure AI Foundry portal

• Introduction
• Explore the model catalog
• Deploy a model to an endpoint
• Optimize model performance
• Exercise - Explore, deploy, and chat with language models
• Module assessment
• Summary

Section 3: Develop an AI app with the Azure AI Foundry SDK

• Introduction
• What is the Azure AI Foundry SDK?
• Work with project connections
• Create a chat client
• Exercise - Create a generative AI chat app
• Module assessment
• Summary

Section 4: Get started with prompt flow to develop language model apps in the Azure AI Foundry

• Introduction
• 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
• Summary

Section 5: Develop a RAG-based solution with your own data using Azure AI Foundry

1. Introduction
2. Understand how to ground your language model
3. Make your data searchable
4. Create a RAG-based client application
5. Implement RAG in a prompt flow
6. Exercise - Create a generative AI app that uses your own data
7. Module assessment
8. Summary

Section 6: Fine-tune a language model with Azure AI Foundry

• Introduction
• 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
• Summary

Section 7: Implement a responsible generative AI solution in Azure AI Foundry

• Introduction
• 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
• Summary

Section 8: Evaluate generative AI performance in Azure AI Foundry portal

• Introduction
• Assess the model performance
• Manually evaluate the performance of a model
• Automated evaluations
• Exercise - Evaluate generative AI model performance
• Module assessment
• Summary