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Azure Databricks is a cloud-scale platform for data analytics and machine learning. Data scientists and machine learning engineers can use Azure Databricks to implement machine learning solutions at scale.


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.


1 – Explore Azure Databricks

  • Get started with Azure Databricks
  • Identify Azure Databricks workloads
  • Understand key concepts

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

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

4 – Use MLflow in Azure Databricks

  • Capabilities of MLflow
  • Run experiments with MLflow
  • Register and serve models with MLflow

5 – Tune hyperparameters in Azure Databricks

  • Optimize hyperparameters with Hyperopt
  • Review Hyperopt trials
  • Scale Hyperopt trials

6 – Use AutoML in Azure Databricks

  • What is AutoML?
  • Use AutoML in the Azure Databricks user interface
  • Use code to run an AutoML experiment

7 – Train deep learning models in Azure Databricks

  • Understand deep learning concepts
  • Train models with PyTorch
  • Distribute PyTorch training with Horovod

Additional information


1 day

Guaranteed to run