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Microsoft DP-3014 - Implementing a Machine Learning Solution with Azure Databricks

  • Length 1 day
  • Price  $990 inc GST
Course overview
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Why study this course

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.

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What you’ll learn

After completing this course, students will be able to:

  • Provision an Azure Databricks workspace

  • Identify core workloads and personas for Azure Databricks

  • Describe key concepts of an Azure Databricks solution

  • Describe key elements of the Apache Spark architecture

  • Create and configure a Spark cluster

  • Describe use cases for Spark

  • Use Spark to process and analyse data stored in files

  • Use Spark to visualise data

  • Prepare data for machine learning

  • Train a machine learning model

  • Evaluate a machine learning model

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

  • Use MLflow to manage and deploy trained models

  • Use the Hyperopt library to optimise hyperparameters

  • Distribute hyperparameter tuning across multiple worker nodes

  • Use the AutoML user interface in Azure Databricks

  • Use the AutoML API in Azure Databricks

  • Train a deep learning model in Azure Databricks

  • Distribute deep learning training by using the Horovod library

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Who is the course for?

  • Data Scientists

  • Machine Learning Engineers

Course subjects

  • 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


  • Experience using Python to explore data and train machine learning models with common open source frameworks, like Scikit-Learn, PyTorch, and TensorFlow. 

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.

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