Designing and Implementing a Data Science Solution on Azure

COURSE OUTLINE

MODULE 01 Explore and configure the Azure Machine Learning workspace

• Explore Azure Machine Learning workspace resources and assets
  • Create an Azure Machine Learning workspace.
  • Identify resources and assets.
  • Train models in the workspace.
• Explore developer tools for workspace interaction
  • The Azure Machine Learning studio.
  • The Python Software Development Kit (SDK).
  • The Azure Command Line Interface (CLI).
• Make data available in Azure Machine Learning
  • Access data by using Uniform Resource Identifiers (URIs).
  • Connect to cloud data sources with datastores.
  • Use data asset to access specific files or folders.
• Work with compute targets in Azure Machine Learning
  • Choose the appropriate compute target.
  • Work with compute instances and clusters.
  • Manage installed packages with environments.
• Work with environments in Azure Machine Learning
• • Understand environments in Azure Machine Learning.
  • Explore and use curated environments.
  • Create and use custom environments.

MODULE 02 Experiment with Azure Machine Learning

• Find the best classification model with Automated Machine Learning
  • Prepare your data to use AutoML for classification.
  • Configure and run an AutoML experiment.
  • Evaluate and compare models.
• Track model training in Jupyter notebooks with MLflow
  • Configure to use MLflow in notebooks
  • Use MLflow for model tracking in notebooks

MODULE 03 Optimize model training with Azure Machine Learning

• Run a training script as a command job in Azure Machine Learning
  • Convert a notebook to a script.
  • Test scripts in a terminal.
  • Run a script as a command job.
  • Use parameters in a command job.
• Track model training with MLflow in jobs
  • Use MLflow when you run a script as a job.
  • Review metrics, parameters, artifacts, and models from a run.
• Perform hyperparameter tuning with Azure Machine Learning
  • Define a hyperparameter search space.
  • Configure hyperparameter sampling.
  • Select an early-termination policy.
  • Run a sweep job.
• Run pipelines in Azure Machine Learning
• • Create components.
  • Build an Azure Machine Learning pipeline.
  • Run an Azure Machine Learning pipeline.

MODULE 04 Manage and review models in Azure Machine Learning

• Register an MLflow model in Azure Machine Learning
  • Log models with MLflow.
  • Understand the MLmodel format.
  • Register an MLflow model in Azure Machine Learning.
• Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
  • Understand Azure Machine Learning’s built-in components for responsible AI.
  • Create a Responsible AI dashboard.
  • Explore a Responsible AI dashboard.

MODULE 05 Deploy and consume models with Azure Machine Learning

• Deploy a model to a managed online endpoint
  • Use managed online endpoints.
  • Deploy your MLflow model to a managed online endpoint.
  • Deploy a custom model to a managed online endpoint.
  • Test online endpoints.
• Deploy a model to a batch endpoint
  • Create a batch endpoint.
  • Deploy your MLflow model to a batch endpoint.
  • Deploy a custom model to a batch endpoint.
  • Invoke batch endpoints.

MODULE 06 Develop generative AI apps in Azure

• Plan and prepare to develop AI solutions on Azure
  • Identify common AI capabilities that you can implement in applications
  • Describe Azure AI Services and considerations for using them
  • Describe Azure AI Foundry and considerations for using it
  • Identify appropriate developer tools and SDKs for an AI project
  • Describe considerations for responsible AI
• Choose and deploy models from the model catalog in Azure AI Foundry portal
  • Select a language model from the model catalog.
  • Deploy a model to an endpoint.
  • Test a model and improve the performance of the model.
• Develop an AI app with the Azure AI Foundry SDK
  • Describe capabilities of the Azure AI Foundry SDK.
  • Use the Azure AI Foundry SDK to work with connections in projects.
  • Use the Azure AI Foundry SDK to develop an AI chat app.
• Get started with prompt flow to develop language model apps in the Azure AI Foundry
  • Understand the development lifecycle when creating language model applications.
  • Understand what a flow is in prompt flow.
  • Explore the core components when working with prompt flow.
• Develop a RAG-based solution with your own data using Azure AI Foundry
  • Identify the need to ground your language model with Retrieval Augmented Generation (RAG)
  • Index your data with Azure AI Search to make it searchable for language models
  • Build an agent using RAG on your own data in the Azure AI Foundry portal
• Fine-tune a language model with Azure AI Foundry
  • Understand when to fine-tune a model.
  • Prepare your data to fine-tune a chat completion model.
  • Fine-tune a base model in the Azure AI Foundry portal.
• Implement a responsible generative AI solution in Azure AI Foundry
  • 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
• Evaluate generative AI performance in Azure AI Foundry portal
  • Understand model benchmarks.
  • Perform manual evaluations.
  • Assess your generative AI apps with AI-assisted metrics.
  • Configure evaluation flows in the Azure AI Foundry portal.