DP-3014
DP-3014: Build machine learning solutions using Azure Databricks
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1 Day
Prerequisites
This learning path 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.with common open source frameworks, like Scikit-Learn, PyTorch, and TensorFlow.
What you’ll learn in this course
Azure Databricks is a fully managed, cloud-based data analytics platform, which empowers developers to accelerate AI and innovation by simplifying the process of building enterprise-grade data applications. Built as a joint effort by Microsoft and the team that started Apache Spark,
Azure Databricks provides data science, engineering, and analytical teams with a single platform for big data processing and machine learning. In this course, you’ll learn how to use Azure Databricks to train and deploy machine learning models.
Course Outline
1 - Explore Azure Databricks
• Get started with Azure Databricks
• Identify Azure Databricks workloads
• Understand key concepts
• Data governance using Unity Catalog and Microsoft Purview
• Module assessment
2 - Use Apache Spark in Azure Databricks
• Get to know Spark
• Create a Spark cluster
• Use Spark in notebooks
• Use Spark to work with data files
• Visualize data
• Module assessment
3 - Train a machine learning model in Azure Databricks
• 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
• Module assessment
4 - Use MLflow in Azure Databricks
• Capabilities of MLflow
• Run experiments with MLflow
• Register and serve models with MLflow
• Module assessment
5 - Tune hyperparameters in Azure Databricks
• Optimize hyperparameters with Optuna
• Review trials
• Scale hyperparameter optimization
• Module assessment
6 - Use AutoML in Azure Databricks
• What is AutoML?
• Use AutoML in the Azure Databricks user interface
• Use code to run an AutoML experiment
• Module assessment
7 - Train deep learning models in Azure Databricks
• Understand deep learning concepts
• Train models with PyTorch
• Distribute PyTorch training with TorchDistributor
• Module assessment
8 - Manage machine learning in production with Azure Databricks
• Automate your data transformations
• Explore model development
• Explore model deployment strategies
• Explore model versioning and lifecycle management
• Module assessment
Further information
If you would like to know more about this course please contact us
