Learning Objectives
- Explore Azure Databricks
- Introduction to Azure Databricks as a cloud service providing a scalable platform for data analytics.
- Use of Apache Spark in Azure Databricks for performing data transformations, analysis, and visualizations at scale.
- Train a Machine Learning Model in Azure Databricks
- Understanding how data is used for training predictive models in Azure Databricks.
- Overview of the commonly used machine learning frameworks supported by Azure Databricks.
- Use MLflow in Azure Databricks
- Introduction to MLflow as an open-source platform managing the machine learning lifecycle.
- Insight into how MLflow is natively supported in Azure Databricks.
- Tune Hyperparameters in Azure Databricks
- The important role of tuning hyperparameters in machine learning.
- Using the Hyperopt library in Azure Databricks for automated hyperparameters optimization.
- Use AutoML in Azure Databricks
- An overview of AutoML’s role in simplifying the process of building effective machine learning models.
- Insight into how AutoML fits into the Azure Databricks ecosystem.
- Train Deep Learning Models in Azure Databricks
- Understanding deep learning and its use of neural networks for training machine learning models.
- Looking at the complex forecasting, computer vision, natural language processing, and other AI workloads handled by deep learning in Azure Databricks.