Description
Azure Databricks is a cloud-scale 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; and integrate Databricks machine learning processes with Azure Machine Learning.
Audience Profile
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:
Course Outline
Get started with Azure Databricks
Azure Databricks enables you to build highly scalable data processing and machine learning solutions.
Learning objectives
After completing this module, you will be able to:
- Understand Azure Databricks
- Provision Azure Databricks workspaces and clusters
- Work with notebooks in Azure Databricks
Work with data in Azure Databricks
To work with data in Azure Databricks, you can use the dataframe object.
Learning objectives
After completing this module, you will be able to:
- Understand dataframes
- Query dataframes
- Visualize data
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.
Learning objectives
After completing this module, you will be able to:
- Understand machine learning concepts
- Perform data cleaning
- Perform feature engineering
- Perform data scaling
- Perform data encoding
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.
Learning objectives
After completing this module, you will be able to:
- Understand Spark ML
- Train and validate a model
- Use other machine learning frameworks
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.
Learning objectives
After completing this module, you will be able to:
- Understand capabilities of MLflow
- Use MLflow terminology
- Run experiments
Manage machine learning models in Azure Databricks
In Azure Databricks, you can deploy and manage machine learning models that you have trained.
Learning objectives
After completing this module, you will be able to:
- Describe considerations for model management
- Register models
- Manage model versioning
Track Azure Databricks experiments in Azure Machine Learning
Azure Machine Learning is a scalable cloud platform for training, deploying, and managing machine learning solutions.
Learning objectives
After completing this module, you will be able to:
- 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
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.
Learning objectives
After completing this module, you will be able to:
- Describe considerations for model deployment
- Plan for Azure Machine Learning deployment endpoints
- Deploy a model to Azure Machine Learning
- Troubleshoot model deployment
