About This Course:

Azure Databricks offers a collaborative environment where data scientists and engineers can seamlessly integrate with popular tools like Apache Spark™ for big data processing and MLflow for managing the end-to-end machine learning lifecycle

Course Objectives:

• Get started with Azure Databricks
• Identify Azure Databricks workloads
• Understand key concepts
• Get to know Spark
• Create a Spark cluster
• Use Spark in notebooks
• Use Spark to work with data files
• Visualize data
• 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
• Capabilities of MLflow
• Run experiments with MLflow
• Register and serve models with MLflow
• Optimize hyperparameters with Hyperopt
• Review Hyperopt trials
• Scale Hyperopt trials
• What is AutoML?
• Use AutoML in the Azure Databricks user interface
• Use code to run an AutoML experiment
• Understand deep learning concepts
• Train models with PyTorch
• Distribute PyTorch training with Horovod

Audience:

• Data professionals seeking to utilize Azure Databricks for ML
• Data scientists/engineers wanting to apply ML workflows
• Azure users aiming to implement scalable ML solutions
• Professionals preparing for Azure Databricks certifications
• Technical personnel interested in combining Azure services with ML

Prerequisites:

• Fundamental understanding of Azure
• Basic knowledge of machine learning concepts
• Experience with Python programming
• Familiarity with Apache Spark and Databricks platform
• Prior exposure to data processing and ETL tasks