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Course Overview
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
1 - Explore Azure Databricks
- Get started with Azure Databricks
- Identify Azure Databricks workloads
- Understand key concepts
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
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
4 - Use MLflow in Azure Databricks
- Capabilities of MLflow
- Run experiments with MLflow
- Register and serve models with MLflow
5 - Tune hyperparameters in Azure Databricks
- Optimize hyperparameters with Hyperopt
- Review Hyperopt trials
- Scale Hyperopt trials
6 - Use AutoML in Azure Databricks
- What is AutoML?
- Use AutoML in the Azure Databricks user interface
- Use code to run an AutoML experiment
7 - Train deep learning models in Azure Databricks
- Understand deep learning concepts
- Train models with PyTorch
- Distribute PyTorch training with Horovod