Build machine learning solutions using Azure Databricks Eğitimi

This course assumes that you have experience of using Python to explore data and train machine learning models with common open source frameworks, like Scikit-Learn, PyTorch, and TensorFlow. Consider completing the Create machine learning models learning path before starting this one.

• Introduction
• Get started with Azure Databricks
• Identify Azure Databricks workloads
• Understand key concepts
• Exercise - Explore Azure Databricks
• Knowledge check
• Summary

• Introduction
• Get to know Spark
• Create a Spark cluster
• Use Spark in notebooks
• Use Spark to work with data files
• Visualize data
• Exercise - Use Spark in Azure Databricks
• Knowledge check
• Summary

• Introduction
• 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
• Exercise - Train a machine learning model in Azure Databricks
• Knowledge check
• Summary

• Introduction
• Capabilities of MLflow
• Run experiments with MLflow
• Register and serve models with MLflow
• Exercise - Use MLflow in Azure Databricks
• Knowledge check
• Summary

• Introduction
• Optimize hyperparameters with Hyperopt
• Review Hyperopt trials
• Scale Hyperopt trials
• Exercise - Optimize hyperparameters for machine learning in Azure Databricks
• Knowledge check
• Summary

• Introduction
• What is AutoML?
• Use AutoML in the Azure Databricks user interface
• Use code to run an AutoML experiment
• Exercise - Use AutoML in Azure Databricks
• Knowledge check
• Summary

• Introduction
• Understand deep learning concepts
• Train models with PyTorch
• Distribute PyTorch training with Horovod
• Exercise - Train deep learning models on Azure Databricks
• Knowledge check
• Summary