DP-3014 Training Course

Explore Azure Databricks

• Provision an Azure Databricks workspace
• Identify core workloads and personas for Azure Databricks
• Describe key concepts of an Azure Databricks solution
• Lab: Explore Azure Databricks

 
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
• Lab: Use Spark in Azure Databricks

 
Train a Machine Learning Model in Azure Databricks

• Prepare data for machine learning
• Train a machine-learning model
• Evaluate a machine learning model
• Lab: Train a machine learning model in Azure Databricks

 
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
• Lab: Use MLflow in Azure Databricks

 
Tune hyperparameters in Azure Databricks

• Use the Hyperopt library to optimize hyperparameters
• Distribute hyperparameter tuning across multiple worker nodes
• Lab: Optimize hyperparameters for machine learning in Azure Databricks

 
Use AutoML in Azure Databricks

• Use the AutoML user interface in Azure Databricks
• Use the AutoML API in Azure Databricks
• Lab: Use AutoML in Azure Databricks

 
Train Deep Learning Models in Azure Databricks

• Train a deep learning model in Azure Databricks
• Distribute deep learning training by using the Horovod library
• Lab: Train deep learning models on Azure Databricks

• Data Engineers
• Data Scientists
• Ideal for professionals aiming for Azure Databricks certifications
• Suitable for tech personnel integrating Azure services with ML

• A fundamental understanding of Azure is required
• Basic knowledge of machine learning concepts
• Experience with Python programming essential
• Familiarity with Apache Spark and the Databricks platform
• Prior experience in data processing and ETL tasks

• Understand how to provision and navigate an Azure Databricks workspace, identifying core workloads and key concepts.
• Learn to create and configure Spark clusters, process and analyze data, and utilize Spark for data visualization.
• Prepare data, train, and evaluate machine learning models using Azure Databricks.
• Implement MLflow for logging experiments, managing, and deploying machine learning models.
• Master the use of the Hyperopt library to optimize machine learning model parameters across multiple nodes.
• Gain proficiency in using AutoML for streamlined model training and deployment within Azure Databricks.
• Train deep learning models and distribute training using the Horovod library for enhanced performance.