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Concevoir et mettre en oeuvre une solution de Data Science dans Azure

  • Référence M-DP100
  • Durée 3 jour(s)

Modalité pédagogique

Options de paiement complémentaires

  • Bons Software Assurance 1 bon de 3 jours

Classe inter en présentiel Prix

EUR1 990,00

hors TVA

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Modalité pédagogique

La formation est disponible dans les formats suivants:

  • Classe inter en présentiel

    Formation délivrée en inter-entreprises. Cette méthode d'apprentissage permet l'interactivité entre le formateur et les participants en classe.

Demander cette formation dans un format différent

Gain the necessary knowledge about how to use Azure services to develop, train, and deploy, machine learning solutions. The course starts with an overview of Azure services that support data science. From there, it focuses on using Azure's premier data science service, Azure Machine Learning service, to automate the data science pipeline. This course is focused on Azure and does not teach the student how to do data science. It is assumed students already know that.

Prochaines dates

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    • Modalité: Classe inter en présentiel
    • Date: 15-17 juillet, 2019
    • Centre: RUEIL ATHENEE

    EUR1 990,00

    • Modalité: Classe inter en présentiel
    • Date: 09-11 septembre, 2019
    • Centre: RUEIL ATHENEE

    EUR1 990,00

    • Modalité: Classe inter en présentiel
    • Date: 04-06 novembre, 2019
    • Centre: RUEIL ATHENEE

    EUR1 990,00

 

This course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models.

Programme détaillé

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Module 1: Doing Data Science on Azure

The students will learn about the data science process and the role of the data scientist. This is then applied to understand how Azure services can support and augment the data science process:

Introduce the Data Science Process

  • Overview of Azure Data Science Options
  • Introduce Azure Notebooks

Module 2: Doing Data Science with Azure Machine Learning Service

The students will learn how to use Azure Machine Learning service to automate the data science process end to end:

  • Introduce Azure Machine Learning (AML) service
  • Register and deploy ML models with AML service

Module 3: Automate Machine Learning with Azure Machine Learning Service

In this module, the students will learn about the machine learning pipeline and how the Azure Machine Learning service's AutoML and HyperDrive can automate some of the laborious parts of it:

  • Automate Machine Learning Model Selection
  • Automate Hyperparameter Tuning with HyperDrive

Module 4: Manage and Monitor Machine Learning Models with the Azure Machine Learning Service

In this module, the student will learn how to automatically manage and monitor machine learning models in the Azure Machine Learning service:

  • Manage and Monitor Machine Learning Models

Pré-requis

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Before attending this course, students must have:

  • Azure Fundamentals
  • Understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one.  
  • How to program in the Python programming language and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn.
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