About this course

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.

Course Outline

Module 1: Explore Azure Databricks.
• Get started with Azure Databricks.
• Identify Azure Databricks workloads.
• Understand key concepts.
• Exercise – Explore Azure Databricks.
Module 2: Use Apache Spark in Azure Databricks.
• Get to know Spark.
• Create a Spark cluster.
• Use Spark in notebooks.
• Use Spark to work with data files.
• Visualize data.
• Exercise – Use Spark in Azure Databricks.
Module 3: Train a machine learning model in Azure Databricks.
• Understand principles of machine learning.
• Machine learning in Azure Databricks.
• Prepare data for machine learning.
• Train a machine learning model.
• Evaluate a machine learning model.
• Exercise – Train a machine learning model in Azure Databricks.
Module 4: Use MLflow in Azure Databricks.
• Capabilities of MLflow.
• Run experiments with MLflow.
• Register and serve models with MLflow.
• Exercise – Use MLflow in Azure Databricks.
Module 5: Tune hyperparameters in Azure Databricks.
• Optimize hyperparameters with Hyperopt.
• Review Hyperopt trials.
• Scale Hyperopt trials.
• Exercise – Optimize hyperparameters for machine learning in Azure Databricks.
Module 6: Use AutoML in Azure Databricks.
• What is AutoML.
• Use AutoML in the Azure Databricks user interface.
• Use code to run an AutoML experiment.
• Exercise – Use AutoML in Azure Databricks.
Module 7: Train deep learning models in Azure Databricks.
• Understand deep learning concepts.
• Train models with PyTorch.
• Distribute PyTorch training with Horovod.
• Exercise – Train deep learning models on Azure Databricks.

Prerequisites

Experience of using Python to explore data and train machine learning models with common open-source frameworks, like Scikit-Learn, PyTorch, and TensorFlow.