Explore Azure Machine Learning workspace resources and assets

As a data scientist, you can use Azure Machine Learning to train and manage your machine learning models. Learn what Azure Machine Learning is, and get familiar with all its 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

Explore developer tools for workspace interaction

Learn how you can interact with the Azure Machine Learning workspace. You can use the Azure Machine Learning studio, the Python SDK (v2), or the Azure CLI (v2).

• Explore the studio
• Explore the Python SDK
• Explore the CLI
• Exercise - Explore the developer tools

Make data available in Azure Machine Learning

Learn about how to connect to data from the Azure Machine Learning workspace. You're introduced to datastores and data assets.

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

Work with compute targets in Azure Machine Learning

Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.

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

Work with environments in Azure Machine Learning

Learn how to use environments in Azure Machine Learning to run scripts on any compute target.

• Understand environments
• Explore and use curated environments
• Create and use custom environments
• Exercise - Work with environments

Find the best classification model with Automated Machine Learning

Learn how to find the best classification model with automated machine learning (AutoML). You'll use the Python SDK (v2) to configure and run an AutoML job.

• 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

Track model training in Jupyter notebooks with MLflow

Learn how to use MLflow for model tracking when experimenting in notebooks.

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

Run a training script as a command job in Azure Machine Learning

Learn how to convert your code to a script and run it 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

Track model training with MLflow in jobs

Learn how to track model training with MLflow in jobs when running scripts.

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

Perform hyperparameter tuning with Azure Machine Learning

Learn how to perform hyperparameter tuning with a sweep job in 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

Run pipelines in Azure Machine Learning

Learn how to create and use components to build pipeline in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows.

• Create components
• Create a pipeline
• Run a pipeline job
• Exercise - Run a pipeline job

Register an MLflow model in Azure Machine Learning

Learn how to log and 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

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

Explore model explanations, error analysis, counterfactuals, and causal analysis by creating a Responsible AI dashboard. You'll create and run the pipeline in Azure Machine Learning using the Python SDK v2 to generate the dashboard.

• Understand Responsible AI
• Create the Responsible AI dashboard
• Evaluate the Responsible AI dashboard
• Exercise - Explore the Responsible AI dashboard

Deploy a model to a managed online endpoint

Learn how to deploy models to a managed online endpoint for real-time inferencing.

• 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

Deploy a model to a batch endpoint

Learn how to deploy models to a batch endpoint. When you invoke a batch endpoint, you trigger a batch scoring job.

• 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

Plan and prepare to develop AI solutions on Azure

Microsoft Azure offers multiple services that enable developers to build amazing AI-powered solutions. Proper planning and preparation involves identifying the services you'll use and creating an optimal working environment for your development team.

• What is AI?
• Foundry Tools
• Microsoft Foundry
• Developer tools and SDKs
• Responsible AI
• Exercise - Prepare for an AI development project

Choose and deploy models from the model catalog in Microsoft Foundry portal

Choose the various language models that are available through the Microsoft Foundry's model catalog. Understand how to select, deploy, and test a model, and to improve its performance.

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

Develop an AI app with the Microsoft Foundry SDK

Use the Microsoft Foundry SDK to develop AI applications with Microsoft Foundry projects.

• What is the Microsoft Foundry SDK?
• Work with project connections
• Create a chat client
• Exercise - Create a generative AI chat app

Get started with prompt flow to develop language model apps in the Microsoft Foundry

Learn about how to use prompt flow to develop applications that leverage language models in the Microsoft 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

Develop a RAG-based solution with your own data using Microsoft Foundry

Retrieval Augmented Generation (RAG) is a common pattern used in generative AI solutions to ground prompts with your data. Microsoft Foundry provides support for adding data, creating indexes, and integrating them with generative AI models to help you build RAG-based solutions.

• 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

Fine-tune a language model with Microsoft Foundry

Train a base language model on a chat-completion task. The model catalog in Microsoft Foundry offers many open-source models that can be fine-tuned for your specific model behavior needs.

• Understand when to fine-tune a language model
• Prepare your data to fine-tune a chat completion model
• Explore fine-tuning language models in Microsoft Foundry portal
• Exercise - Fine-tune a language model

Implement a responsible generative AI solution in Microsoft Foundry

Generative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation.

• 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

Evaluate generative AI performance in Microsoft Foundry portal

Evaluating copilots is essential to ensure your generative AI applications meet user needs, provide accurate responses, and continuously improve over time. Discover how to assess and optimize the performance of your generative AI applications using the tools and features available in the Azure AI Studio.

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