Data Analytics and Machine Learning Solutions with Azure Databricks

Explore Azure Databricks

• Get started with Azure Databricks
• Identify Azure Databricks workloads
• Understand key concepts of Azure Databricks
• Explore data governance using Unity Catalog and Microsoft Purview
• Hands-on exercise: Explore Azure Databricks features and tools

Perform data analysis with Azure Databricks

• Ingest data into Azure Databricks from various sources
• Use data exploration tools in Azure Databricks
• Perform data analysis using DataFrame APIs
• Clean and preprocess data for analysis
• Hands-on exercise: Explore data using Azure Databricks

Use Apache Spark in Azure Databricks

• Introduction to Apache Spark and its capabilities
• Create and configure a Spark cluster in Azure Databricks
• Use Spark in notebooks for data analysis
• Load and process data files with Spark
• Visualize data using Spark DataFrames and plots
• Hands-on exercise: Use Apache Spark in Azure Databricks

Manage data with Delta Lake

• Introduction to Delta Lake and its benefits
• Manage ACID transactions in Delta Lake
• Implement schema enforcement in Delta Lake
• Use data versioning and time travel features in Delta Lake
• Ensure data integrity with Delta Lake
• Hands-on exercise: Work with Delta Lake in Azure Databricks

Build data pipelines with Delta Live Tables

• Explore Delta Live Tables and its features
• Ingest and integrate data into Delta Live Tables pipelines
• Process real-time data with Delta Live Tables
• Build scalable and automated data pipelines
• Hands-on exercise: Create a data pipeline with Delta Live Tables

Deploy workloads with Azure Databricks Workflows

• Introduction to Azure Databricks Workflows and its components
• Understand the key components and benefits of Databricks Workflows
• Deploy workloads using Azure Databricks Workflows
• Automate repetitive tasks and processes
• Hands-on exercise: Create and deploy a workflow in Azure Databricks

Train a machine learning model in Azure Databricks

• Understand the principles and components of machine learning
• Machine learning in Azure Databricks and its integration with Spark
• Prepare and preprocess data for machine learning
• Train and evaluate a machine learning model in Azure Databricks
• Hands-on exercise: Train a machine learning model

Use MLflow in Azure Databricks

• Overview of MLflow and its capabilities
• Run and manage machine learning experiments with MLflow
• Register and serve machine learning models with MLflow
• Track model performance and manage experiment metadata
• Hands-on exercise: Use MLflow for model management in Azure Databricks

Tune hyperparameters in Azure Databricks

• Introduction to hyperparameter optimization with Hyperopt
• Optimize hyperparameters for machine learning models
• Review Hyperopt trials and results
• Scale Hyperopt trials for better performance
• Hands-on exercise: Optimize hyperparameters using Hyperopt

Use AutoML in Azure Databricks

• Introduction to AutoML and its use cases
• Use the Azure Databricks AutoML user interface for model training
• Run AutoML experiments with code
• Evaluate AutoML models and select the best one
• Hands-on exercise: Use AutoML for model training in Azure Databricks

Train deep learning models in Azure Databricks

• Understand deep learning concepts and frameworks
• Train deep learning models using PyTorch in Azure Databricks
• Distribute PyTorch training using TorchDistributor
• Use GPUs for efficient deep learning model training
• Hands-on exercise: Train deep learning models on Azure Databricks

Manage machine learning in production with Azure Databricks

• Automate data transformations in machine learning workflows
• Explore model development best practices
• Learn deployment strategies for machine learning models
• Implement model versioning and lifecycle management
• Hands-on exercise: Manage a machine learning model in production