Implementing a Machine Learning solution with Azure Databricks (DP-3014)

Learn to train, tune and manage machine learning models with Azure Databricks

Azure Databricks is a cloud-scale platform for data analytics and machine learning. Data scientists and machine learning engineers can use Azure Databricks to implement machine learning solutions at scale.
- Provision an Azure Databricks workspace.
- Identify core workloads and personas for Azure Databricks.
- Describe key concepts of an Azure Databricks solution.

- 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.

- Prepare data for machine learning
- Train a machine learning model
- Evaluate a machine learning model

- Use MLflow to log parameters, metrics, and other details from experiment runs.
- Use MLflow to manage and deploy trained models.

- Use the Hyperopt library to optimize hyperparameters.
- Distribute hyperparameter tuning across multiple worker nodes.

- Use the AutoML user interface in Azure Databricks
- Use the AutoML API in Azure Databricks

- Train a deep learning model in Azure Databricks
- Distribute deep learning training by using the Horovod library