Home/Courses/Microsoft Data & Power BI/DP-3014 Build machine learning solutions using Azure Databri…
Microsoft Data & Power BIDP-3014 Build machine learning solutions using Azure Databricks
Apply machine learning on Azure Databricks in one day: use Spark, train and tune models, try AutoML and deep learning, track experiments with MLflow and manage models in production.
1 dayIntermediate8 modulesDP-3014
About this course
Apply machine learning on Azure Databricks in one day: use Spark, train and tune models, try AutoML and deep learning, track experiments with MLflow and manage models in production.
Curriculum
8 modules
- Get started with Azure Databricks
- Identify Azure Databricks workloads
- Understand key concepts
- Data governance using Unity Catalog and Microsoft Purview
- Module assessment
- Get to know Spark
- Create a Spark cluster
- Use Spark in notebooks
- Use Spark to work with data files
- Visualize data
- Module assessment
- 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
- Capabilities of MLflow
- Run experiments with MLflow
- Register and serve models with MLflow
- Module assessment
- Optimize hyperparameters with Optuna
- Review trials
- Scale hyperparameter optimization
- Module assessment
- What is AutoML?
- Use AutoML in the Azure Databricks user interface
- Use code to run an AutoML experiment
- Module assessment
- Understand deep learning concepts
- Train models with PyTorch
- Distribute PyTorch training with TorchDistributor
- Module assessment
- Automate your data transformations
- Explore model development
- Explore model deployment strategies
- Explore model versioning and lifecycle management
- Module assessment
Who it's for
Data scientists who train models in Python and want to scale them on Databricks.
Before you start
- 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.
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