Microsoft Course 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.

Who Should Attend?

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.

Course Prerequisites

• Creating cloud resources in Microsoft Azure.
• Using Python to explore and visualize data.
• Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
• Working with containers

Course Outline

1 - Design a data ingestion strategy for machine learning projects

• Identify your data source and format
• Choose how to serve data to machine learning workflows
• Design a data ingestion solution

2 - Design a machine learning model training solution

• Identify machine learning tasks
• Choose a service to train a machine learning model
• Decide between compute options

3 - Design a model deployment solution

• Understand how model will be consumed
• Decide on real-time or batch deployment

4 - Design a machine learning operations solution

• Explore an MLOps architecture
• Design for monitoring
• Design for retraining

5 - 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

6 - Explore developer tools for workspace interaction

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

7 - Make data available in Azure Machine Learning

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

8 - 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

9 - Work with environments in Azure Machine Learning

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

10 - Find the best classification model with Automated Machine Learning

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

11 - Track model training in Jupyter notebooks with MLflow

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

12 - 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

13 - Track model training with MLflow in jobs

• Track metrics with MLflow
• View metrics and evaluate models

14 - 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

15 - Run pipelines in Azure Machine Learning

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

16 - Register an MLflow model in Azure Machine Learning

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

17 - 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

18 - 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

19 - 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

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