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DP-3014: Implementing a Machine Learning solution with Azure Databrick

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590€

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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.

Objetivos

Students will learn to:

  • Explore Azure Databricks
  • Use Apache Spark in Azure Databricks
  • Train a machine learning model in Azure Databricks
  • Use MLflow in Azure Databricks
  • Tune hyperparameters in Azure Databricks
  • Use AutoML in Azure Databricks
  • Train deep learning models in Azure Databricks

Pré-Requisitos

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. Consider completing the Create machine learning models learning path before starting this one.

Programa

  • Explore Azure Databricks

  • Use Apache Spark in Azure Databricks

  • Train a machine learning model in Azure Databricks

  • Use MLflow in Azure Databricks

  • Tune hyperparameters in Azure Databricks

  • Use AutoML in Azure Databricks

  • Train deep learning models in Azure Databricks

Explore Azure Databricks

  • Provision an Azure Databricks workspace.

  • Identify core workloads and personas for Azure Databricks.

  • Describe key concepts of an Azure Databricks solution.

Use Apache Spark in Azure Databricks

  • Describe key elements of the Apache Spark architecture.

  • Create and configure a Spark cluster.

  • Describe use cases for Spark.

  • Use Spark to process and analyze data stored in files.

  • Use Spark to visualize data.

Train a machine learning model in Azure Databricks

  • Prepare data for machine learning

  • Train a machine learning model

  • Evaluate a machine learning model

Use MLflow in Azure Databricks

  • Use MLflow to log parameters, metrics, and other details from experiment runs.

  • Use MLflow to manage and deploy trained models.

Tune hyperparameters in Azure Databricks

  • Use the Hyperopt library to optimize hyperparameters.

  • Distribute hyperparameter tuning across multiple worker nodes.

Use AutoML in Azure Databricks

  • Use the AutoML user interface in Azure Databricks

  • Use the AutoML API in Azure Databricks

Train deep learning models in Azure Databricks

  • Train a deep learning model in Azure Databricks

  • Distribute deep learning training by using the Horovod library

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DP-3014: Implementing a Machine Learning solution with Azure Databrick

Data & AI | 7h - e-learning

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