Perform data science with Azure Databricks
Created By
Microsoft via Microsoft Learn
- 0
- 8-9 hours worth of material
- Microsoft Learn
- English
Course Overview
- Module 1: Describe Azure Databricks
- Understand the Azure Databricks platform
- Create your own Azure Databricks workspace
- Create a notebook inside your home folder in Databricks
- Understand the fundamentals of Apache Spark notebook
- Create, or attach to, a Spark cluster
- Identify the types of tasks well-suited to the unified analytics engine Apache Spark
- Module 2: Spark architecture fundamentals
- Understand the architecture of an Azure Databricks Spark Cluster
- Understand the architecture of a Spark Job
- Module 3: Read and write data in Azure Databricks
- Use Azure Databricks to read multiple file types, both with and without a Schema.
- Combine inputs from files and data stores, such as Azure SQL Database.
- Transform and store that data for advanced analytics.
- Module 4: Work with DataFrames in Azure Databricks
- Use the count() method to count rows in a DataFrame
- Use the display() function to display a DataFrame in the Notebook
- Cache a DataFrame for quicker operations if the data is needed a second time
- Use the limit function to display a small set of rows from a larger DataFrame
- Use select() to select a subset of columns from a DataFrame
- Use distinct() and dropDuplicates to remove duplicate data
- Use drop() to remove columns from a DataFrame
- Module 5: Work with user-defined functions
- Write User-Defined Functions
- Perform ETL operations using User-Defined Functions
- Module 6: Build and query a Delta Lake
- Learn about the key features and use cases of Delta Lake.
- Use Delta Lake to create, append, and upsert tables.
- Perform optimizations in Delta Lake.
- Compare different versions of a Delta table using Time Machine.
- Module 7: Perform machine learning with Azure Databricks
- Perform Machine Learning
- Train a model and create predictions
- Perform exploratory data analysis
- Describe machine learning workflows
- Build and evaluate machine learning models
- Module 8: Train a machine learning model
- Perform featurization of the dataset
- Finish featurization of the dataset
- Understand Regression modeling
- Build and interpret a regression model
- Module 9: Work with MLflow in Azure Databricks
- Use MLflow to track experiments, log metrics, and compare runs
- Work with MLflow to track experiment metrics, parameters, artifacts and models.
- Module 10: Perform model selection with hyperparameter tuning
- Describe Model selection and Hyperparameter Tuning
- Select the optimal model by tuning Hyperparameters
- Module 11: Deep learning with Horovod for distributed training
- Use Horovod to train a deep learning model
- Use Petastorm to read datasets in Apache Parquet format with Horovod for distributed model training
- Work with Horovod and Petastorm for training a deep learning model
- Module 12: Work with Azure Machine Learning to deploy serving models
- Use Azure Machine Learning to deploy Serving Models
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Course Circullum
- Module 1: Describe Azure Databricks
- Introduction
- Explain Azure Databricks
- Create an Azure Databricks workspace and cluster
- Understand Azure Databricks Notebooks
- Exercise: Work with Notebooks
- Knowledge check
- Summary
- Module 2: Spark architecture fundamentals
- Introduction
- Understand the architecture of Azure Databricks spark cluster
- Understand the architecture of spark job
- Knowledge check
- Summary
- Module 3: Read and write data in Azure Databricks
- Introduction
- 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
- Knowledge check
- Summary
- Module 4: Work with DataFrames in Azure Databricks
- Introduction
- Describe a DataFrame
- Use common DataFrame methods
- Use the display function
- Exercise: Distinct articles
- Knowledge check
- Summary
- Module 5: Work with user-defined functions
- Introduction
- Write user defined functions
- Exercise: Perform Extract, Transform, Load(ETL) operations using user-defined functions
- Knowledge check
- Summary
- Module 6: Build and query a Delta Lake
- Introduction
- Describe the open source Delta Lake
- Exercise: Work with basic Delta Lake functionality
- Describe how Azure Databricks manages Delta Lake
- Exercise: Use the Delta Lake Time Machine and perform optimization
- Knowledge check
- Summary
- Module 7: Perform machine learning with Azure Databricks
- Introduction
- 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
- Knowledge check
- Summary
- Module 8: Train a machine learning model
- Introduction
- Perform featurization of the dataset
- Exercise: Finish featurization of the dataset
- Understand regression modeling
- Exercise: Build and interpret a regression model
- Knowledge check
- Summary
- Module 9: Work with MLflow in Azure Databricks
- Introduction
- Use MLflow to track experiments, log metrics, and compare runs
- Exercise: Work with MLflow to track experiment metrics, parameters, artifacts and models
- Knowledge check
- Summary
- Module 10: Perform model selection with hyperparameter tuning
- Introduction
- Describe model selection and hyperparameter tuning
- Exercise: Select optimal model by tuning hyperparameters
- Knowledge check
- Summary
- Module 11: Deep learning with Horovod for distributed training
- Introduction
- Use Horovod to train a deep learning model
- Use Petastorm to read datasets in Apache Parquet format with Horovod for distributed model training
- Exercise: Work with Horovod and Petastorm for training a deep learning model
- Knowledge check
- Summary
- Module 12: Work with Azure Machine Learning to deploy serving models
- Introduction
- Use Azure Machine Learning to deploy serving models
- Knowledge check
- Summary
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This Course Include:
- Module 1: Describe Azure Databricks
- Introduction
- Explain Azure Databricks
- Create an Azure Databricks workspace and cluster
- Understand Azure Databricks Notebooks
- Exercise: Work with Notebooks
- Knowledge check
- Summary
- Module 2: Spark architecture fundamentals
- Introduction
- Understand the architecture of Azure Databricks spark cluster
- Understand the architecture of spark job
- Knowledge check
- Summary
- Module 3: Read and write data in Azure Databricks
- Introduction
- 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
- Knowledge check
- Summary
- Module 4: Work with DataFrames in Azure Databricks
- Introduction
- Describe a DataFrame
- Use common DataFrame methods
- Use the display function
- Exercise: Distinct articles
- Knowledge check
- Summary
- Module 5: Work with user-defined functions
- Introduction
- Write user defined functions
- Exercise: Perform Extract, Transform, Load(ETL) operations using user-defined functions
- Knowledge check
- Summary
- Module 6: Build and query a Delta Lake
- Introduction
- Describe the open source Delta Lake
- Exercise: Work with basic Delta Lake functionality
- Describe how Azure Databricks manages Delta Lake
- Exercise: Use the Delta Lake Time Machine and perform optimization
- Knowledge check
- Summary
- Module 7: Perform machine learning with Azure Databricks
- Introduction
- 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
- Knowledge check
- Summary
- Module 8: Train a machine learning model
- Introduction
- Perform featurization of the dataset
- Exercise: Finish featurization of the dataset
- Understand regression modeling
- Exercise: Build and interpret a regression model
- Knowledge check
- Summary
- Module 9: Work with MLflow in Azure Databricks
- Introduction
- Use MLflow to track experiments, log metrics, and compare runs
- Exercise: Work with MLflow to track experiment metrics, parameters, artifacts and models
- Knowledge check
- Summary
- Module 10: Perform model selection with hyperparameter tuning
- Introduction
- Describe model selection and hyperparameter tuning
- Exercise: Select optimal model by tuning hyperparameters
- Knowledge check
- Summary
- Module 11: Deep learning with Horovod for distributed training
- Introduction
- Use Horovod to train a deep learning model
- Use Petastorm to read datasets in Apache Parquet format with Horovod for distributed model training
- Exercise: Work with Horovod and Petastorm for training a deep learning model
- Knowledge check
- Summary
- Module 12: Work with Azure Machine Learning to deploy serving models
- Introduction
- Use Azure Machine Learning to deploy serving models
- Knowledge check
- Summary
- Provider:Microsoft Learn
- Certificate:Not Avalible
- Language:English
- Duration:8-9 hours worth of material
- Language CC: