DP-100T01 Designing and Implementing a Data Science Solution on Azure

1 – 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

2 – Explore developer tools for workspace interaction

• Explore the studio
• Explore the Python SDK
• Explore the CLI

3 – Make data available in Azure Machine Learning

• Understand URIs
• Create a datastore
• Create a data asset

4 – 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

5 – Work with environments in Azure Machine Learning

• Understand environments
• Explore and use curated environments
• Create and use custom environments

6 – Find the best classification model with Automated Machine Learning

• Preprocess data and configure featurization
• Run an Automated Machine Learning experiment
• Evaluate and compare models

7 – Track model training in Jupyter notebooks with MLflow

• Configure MLflow for model tracking in notebooks
• Train and track models in notebooks

8 – 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

9 – Track model training with MLflow in jobs

• Track metrics with MLflow
• View metrics and evaluate models

10 – 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

11 – Run pipelines in Azure Machine Learning

• Create components
• Create a pipeline
• Run a pipeline job

12 – Register an MLflow model in Azure Machine Learning

• Log models with MLflow
• Understand the MLflow model format
• Register an MLflow model

13 – 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

14 – 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

15 – 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

16 – Introduction to Azure AI Foundry

• What is Azure AI Foundry?
• How does Azure AI Foundry work
• When to use Azure AI Foundry

17 – Explore and deploy models from the model catalog in Azure AI Foundry portal

• Explore the language models in the model catalog
• Deploy a model to an endpoint
• Improve the performance of a language model

18 – 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

19 – Build a RAG-based agent with your own data using Azure AI Foundry

• Understand how to ground your language model
• Make your data searchable
• Build an agent with prompt flow

20 – 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

21 – Evaluate the performance of generative AI apps with Azure AI Foundry

• Assess the model performance
• Manually evaluate the performance of a model
• Assess the performance of your generative AI apps

22 – Responsible generative AI

• Plan a responsible generative AI solution
• Identify potential harms
• Measure potential harms
• Mitigate potential harms
• Operate a responsible generative AI solution