DP-090: Implementing a Machine Learning Solution with Microsoft Azure Databricks
nanforiberica



Course description
Azure Databricks is a cloud-scalable platform for data analytics and machine learning. In this one-day course, you'll learn how to use Azure Databricks to explore, prepare, and model data, as well as how to integrate Databricks machine learning processes with Azure Machine Learning.
Audience Profile
This course is designed for data scientists with a Python background who need to learn how to apply their data science and machine learning skills to Azure Databricks.
Items in this collection
- Introduction to Azure Databricks (7 Units)
- Work with data in Azure Databricks (7 Units)
- Prepare data for machine learning with Azure Databricks (9 Units)
- Training a machine learning model with Azure Databricks (7 Units)
- Using MLflow to track experiments on Azure Databricks (7 Units)
- Managing machine learning models in Azure Databricks (7 Units)
- Tracking of Azure Databricks experiments in Azure Machine Learning (8 Units)
- Implementation of Azure Databricks models in Azure Machine Learning (8 Units)
course outline
Module 1: Introduction to Azure Databricks
In this module, you'll learn how to provision an Azure Databricks cluster and workspace and use them to work with data.
lessons
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Introduction to Azure Databricks
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Work with data in Azure Databricks
Lab : Introduction to Azure Databricks
Lab : Working with data in Azure Databricks
After completing this module, you will be able to:
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Provision an Azure Databricks cluster and workspace
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Using Azure Databricks to work with data
Module 2: Training and evaluating Machine Learning models
In this module, you'll learn how to use Azure Databricks to prepare data for modeling, and how to train and validate a machine learning model.
lessons
-
Preparing the data for machine learning
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Training a Machine Learning model
Lab : Training a Machine Learning Model
Lab : Preparing the data for machine learning
After completing this module, you will be able to use Azure Databricks to:
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Data preparation for modeling
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Train and validate a machine learning model
Module 3: Managing Experiments and Models
In this module, you'll learn how to use MLflow to track experiments running on Azure Databricks and how to manage Machine Learning models.
lessons
-
Using MLflow to track experiments
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Model Management
Lab : Using MLflow to track experiments
Lab : Managing Models
After completing this module, you will be able to:
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Use MLflow to track experiments
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Model Management
Module 4: Azure Databricks and Azure Machine Learning integration
In this module, you will learn how to integrate Azure Databricks with Azure Machine Learning.
lessons
-
Track experiments with Azure Machine Learning
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Deploy models
Lab : Deploy models to Azure Machine Learning
Lab : Running experiments in Azure Machine Learning
After completing this module, you will be able to:
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Run Azure Machine Learning experiments on Azure Databricks Compute
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Deploy to Azure Machine Learning models trained on Azure Databricks
Previous requirements
Before participating in this course, you should have experience using Python to work with data and some knowledge of machine learning concepts. Before participating in this course, please complete the following learning path in Microsoft Learn:
- Building Machine Learning models
Language
- English course
- Labs: English