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Description

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Overview:

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.

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.

Outline:

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

Length

1 day

Guaranteed to run

Yes