Implementing a Machine Learning Solution with Azure Databricks (DP-3014)
Data scientists and machine learning engineers can use Azure Databricks to implement machine learning solutions at scale.
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Prerequisites:
This learning path assumes that you have experience of using Python to explore data and train machine learning models with common Open-Source frameworks, like Scikit-Learn, PyTorch, and TensorFlow. Consider completing the Create machine learning models learning path before starting this one.
Target Audience:
Data scientists and machine learning engineers.
Course Objectives:
Students will learn to:
- Explore Azure Databricks
- Use Apache Spark in Azure Databricks
- Train a machine learning model in Azure Databricks
- Use MLflow in Azure Databricks
- Tune hyperparameters in Azure Databricks
- Use AutoML in Azure Databricks
- Train deep learning models in Azure Databricks
Course Outline:
Module 1: Explore Azure Databricks
- Provision an Azure Databricks workspace.
- Identify core workloads and personas for Azure Databricks.
- Describe key concepts of an Azure Databricks solution.
Module 2: Use Apache Spark in Azure Databricks
- Describe key elements of the Apache Spark architecture.
- Create and configure a Spark cluster.
- Describe use cases for Spark.
- Use Spark to process and analyze data stored in files.
- Use Spark to visualize data.
Module 3: Train a machine learning model in Azure Databricks
- Prepare data for machine learning
- Train a machine learning model
- Evaluate a machine learning model
Module 4: Use MLflow in Azure Databricks
- Use MLflow to log parameters, metrics, and other details from experiment runs.
- Use MLflow to manage and deploy trained models.
Module 5: Tune hyperparameters in Azure Databricks
- Use the Hyperopt library to optimize hyperparameters.
- Distribute hyperparameter tuning across multiple worker nodes.
Module 6: Use AutoML in Azure Databricks
- Use the AutoML user interface in Azure Databricks
- Use the AutoML API in Azure Databricks
Module 7: Train deep learning models in Azure Databricks
- Train a deep learning model in Azure Databricks
- Distribute deep learning training by using the Horovod library