DP-090: Implementing a Machine Learning Solution with Microsoft Azure Databricks
DP-090: Implementing a Machine Learning Solution with Microsoft Azure Databricks
Objectives
- Understand Azure Databricks
- Provision Azure Databricks workspaces and clusters
- Work with notebooks in Azure Databricks
- Understand dataframes
- Query dataframes
- Visualize data
- Understand machine learning concepts
- Perform data cleaning
- Perform feature engineering
- Perform data scaling
- Perform data encoding
- Understand Spark ML
- Train and validate a model
- Use other machine learning frameworks
- Understand capabilities of MLflow
- Use MLflow terminology
- Run experiments
- Describe considerations for model management
- Register models
- Manage model versioning
- Describe Azure Machine Learning
- Run Azure Databricks experiments in Azure Machine Learning
- Log metrics in Azure Machine Learning with MLflow
- Run Azure Machine Learning pipelines on Azure Databricks compute
- Describe considerations for model deployment
- Plan for Azure Machine Learning deployment endpoints
- Deploy a model to Azure Machine Learning
- Troubleshoot model deployment
Content
1. Get started with Azure Databricks
Azure Databricks enables you to build highly scalable data processing and machine learning solutions.
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2. Work with data in Azure Databricks
To work with data in Azure Databricks, you can use the dataframe object.
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3. Prepare data for machine learning with Azure Databricks
Before using data to train a machine learning model, it's important to prepare the data appropriately.
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4. Train a machine learning model with Azure Databricks
Machine learning involves using data to train a predictive model. Azure Databricks support multiple commonly used machine learning frameworks that you can use to train models.
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5. Use MLflow to track experiments in Azure Databricks
When you run data science and machine learning experiments at scale, you can use MLflow to track experiment runs and metrics.
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6. Manage machine learning models in Azure Databricks
In Azure Databricks, you can deploy and manage machine learning models that you have trained.
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7. Track Azure Databricks experiments in Azure Machine Learning
Azure Machine Learning is a scalable cloud platform for training, deploying, and managing machine learning solutions.
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8. Deploy Azure Databricks models in Azure Machine Learning
You can use Azure Databricks to train machine learning models, and deploy the trained models in Azure Machine Learning endpoints.
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Audience
This course is designed for data scientists with experience of Pythion who need to learn how to apply their data science and machine learning skills on Azure Databricks.
Prerequisites
Before attending this course, you should have experience of using Python to work with data, and some knowledge of machine learning concepts. Before attending this course, complete the following learning path on Microsoft Learn:
- Create machine learning models
Certification
This course is not associated with any Certification.
Schedule
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