DP-100T01-A | 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.
 
Audience profile
This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.
 
Role
Data Scientist
 

Course outline
 
Module 1: Explore and configure the Azure Machine Learning workspace
Throughout this learning path you explore and configure the Azure Machine Learning workspace. Learn how you can create a workspace and what you can do with it. Explore the various developer tools you can use to interact with the workspace. Configure the workspace for machine learning workloads by creating data assets and compute resources.
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
Module 2: Experiment with Azure Machine Learning
Learn how to find the best model with automated machine learning (AutoML) and by experimenting in notebooks.
Find the best classification model with Automated Machine Learning
Track model training in Jupyter notebooks with MLflow
Module 3: Optimize model training with Azure Machine Learning
Learn how to optimize model training in Azure Machine Learning by using scripts, jobs, components and pipelines.
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
Module 4: Manage and review models in Azure Machine Learning
Learn how to manage and review models in Azure Machine Learning by using MLflow to store your model files and using responsible AI features to evaluate your models.
Register an MLflow model in Azure Machine Learning
Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
Module 5: Deploy and consume models with Azure Machine Learning
Learn how to deploy a model to an endpoint. When you deploy a model, you can get real-time or batch predictions by calling the endpoint.
Deploy a model to a managed online endpoint
Deploy a model to a batch endpoint
Module 6: Develop generative AI apps in Azure AI Foundry portal
Generative Artificial Intelligence (AI) is becoming more accessible through easy-to-use platforms like Azure AI Foundry. Learn how to build generative AI applications that use language models with prompt flow to provide value to your users.
Plan and prepare to develop AI solutions on Azure
Choose and deploy models from the model catalog in Azure AI Foundry portal
Develop an AI app with the Azure AI Foundry SDK
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
 
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