Introduction to Azure Databricks-Welcome to the Course
Introduction to the course
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Course syllabus
How to be successful in this course
Introduction to Azure Databricks-Describe Azure Databricks
Explain Azure Databricks
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Create an Azure Databricks workspace and cluster
Create and execute a notebook
Exercise: Work with Notebooks
Lesson summary
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Introduction to Azure Databricks-Spark architecture fundamentals
Lesson introduction
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Understand the architecture of Azure Databricks Spark cluster
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Understand the architecture of spark job
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Lesson summary
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Working with data in Azure Databricks-Use Azure Databricks to prepare the data for advanced analytics and machine learning operations
Lesson introduction
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Read data in CSV format
Read data in JSON format
Read data in Parquet format
Read data stored in tables and views
Write data
Exercises: Read and write data
Lesson summary
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Working with data in Azure Databricks-Work with DataFrames in Azure Databricks
Lesson introduction
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Describe a DataFrame
Use common DataFrame methods
Use the display function
Exercise: Distinct articles
Lesson summary
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Processing data in Azure Databricks-Build and query a Delta Lake
Describe the open source Delta Lake
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Get started with Delta using Spark APIs
Exercise: Work with basic Delta Lake functionality
Describe how Azure Databricks manages Delta Lake
Exercise: Use the Delta Lake Time Machine and perform optimization
Lesson summary
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Processing data in Azure Databricks-Work with user-defined functions
Lesson introduction
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Write user defined functions
Exercise: Perform Extract, Transform, Load (ETL) operations using user-defined functions
Lesson summary
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Additional resources
Get started with Databricks and machine learning-Perform machine learning with Azure Databricks
Lesson introduction
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Understand machine learning
Exercise: Train a model and create predictions
Understand data using exploratory data analysis
Exercise: Perform exploratory data analysis
Describe machine learning workflows
Exercise: Build and evaluate a baseline machine learning model
Lesson summary
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Get started with Databricks and machine learning-Train a machine learning model
Lesson introduction
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Perform featurization of the dataset
Exercise: Finish featurization of the dataset
Understand regression modeling
Exercise: Build and interpret a regression model
Lesson summary
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Additional resources
Manage machine learning lifecycles and fine tune models-Work with MLflow in Azure Databricks
Lesson introduction
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Use MLflow to track experiments, log metrics, and compare runs
Exercise: Work with MLflow to track experiment metrics, parameters, artifacts and modelss
Lesson summary
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Manage machine learning lifecycles and fine tune models-Perform model selection with hyperparameter tuning
Lesson introduction
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Describe model selection and hyperparameter tuning
Exercise: Select optimal model by tuning hyperparameters
Lesson summary
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Additional resources
Train a distributed neural network and serve models with Azure Machine Learning-Deep learning with Horovod for distributed training
Lesson introduction
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Use Horovod to train a deep learning model
Use Petastorm to read in Apache Parquet format with Horovod for distributed model training
Exercise: Work with Horovod and Petastorm for training a deep learning model
Lesson summary
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Train a distributed neural network and serve models with Azure Machine Learning-Work with Azure Machine Learning to deploy serving models
Lesson introduction
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Use Azure Machine Learning to deploy serving models
Lesson summary
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Additional resources
Train a distributed neural network and serve models with Azure Machine Learning-Course wrap up
Congratulations
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Next steps