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

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.

This course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models.

Before attending this course, students must have:

• Azure Fundamentals
• Understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one.
• How to program in the Python programming language and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn

After completing this course, students will be able to:

• Design and prepare a machine learning solution
• Explore data, and run experiments
• Train and deploy models
• Optimise language models for AI applications

Explore and configure the Azure Machine Learning workspace

• Explore Azure Machine Learning workspace resources and assets
• Explore developer tools for workspace interaction
• Make data available in Azure Machine Learning
• Work with compute targets in Azure Machine Learning
• Work with environments in Azure Machine Learning

Experiment with Azure Machine Learning

• Find the best classification model with Automated Machine Learning
• Track model training in Jupyter notebooks with MLflow

Optimise model training with Azure Machine Learning

• Run a training script as a command job in Azure Machine Learning
• Track model training with MLflow in jobs
• Perform hyperparameter tuning with Azure Machine Learning
• Run pipelines in Azure Machine Learning

Manage and review models in Azure Machine Learning

• Register an MLflow model in Azure Machine Learning
• Create and explore the Responsible AI dashboard for a model in Azure

Deploy and consume models with Azure Machine Learning

• Deploy a model to a managed online endpoint
• Deploy a model to a batch endpoint

Develop generative AI apps in Azure AI Foundry portal

• Introduction to Azure AI Foundry
• Explore and deploy models from the model catalog in Azure AI Foundry portal
• Get started with prompt flow to develop language model apps in the Azure AI Foundry
• Build a RAG-based agent with your own data using Azure AI Foundry
• Fine-tune a language model with Azure AI Foundry
• Evaluate the performance of generative AI apps with Azure AI Foundry
• Responsible generative AI