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  • DP-100: Designing and Implementing a Data Science Solution on Azure (DP-100T01-A) (EN)

  • Group Training

    Are you a motivated data scientist and want to learn how to build and operate machine learning solutions in the cloud? Then this training is for you!

    Training code
    CGADP100CE
    Spoken Language
    English
    Language Materials
    English
    Dayparts
    6
    Price
    €2.000,00
    excl. VAT No extra costs.

    Book DP-100: Designing and Implementing a Data Science Solution on Azure (DP-100T01-A) (EN) now

    In group training, we use several learning methods to help you obtain the knowledge, give you helpful insights and get you inspired. Check the Spoken language and Language materials on the left for language info.

    This course currently isn't planned. Please fill in your contact details below and we'll get in touch with you within two working days.

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    What is DP-100: Designing and Implementing a Data Science Solution on Azure (DP-100T01-A)

    Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

     
     

    Who should attend DP-100: Designing and Implementing a Data Science Solution on Azure (DP-100T01-A)

    This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

    Prerequisites

    Before attending this course, students must have:
    A fundamental knowledge of Microsoft Azure

    • Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
    • Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.

    Objectives

    At the end of the training, you will be able to:

    • Provision an Azure Machine Learning workspace
    • Use tools and code to work with Azure Machine Learning
    • Use designer to train a machine learning model
    • Deploy a designer pipeline as a service
    • Run code-based experiments in an Azure Machine Learning workspace
    • Train and register machine learning models
    • Create and consume Datastores
    • Create and consume Datasets
    • Create and use environments
    • Create and use compute targets
    • Create pipelines to automate machine learning workflows
    • Publish and run pipeline services
    • Publish a model as a real-time inference service
    • Publish a model as a batch inference service
    • Optimize hyperparameters for model training
    • Use automated machine learning to find the optimal model for your data
    • Generate model explanations with automated machine learning
    • Use explainers to interpret machine learning models
    • Use application insights to monitor a published model
    • Monitor data drift

    Exam information


    • Exam duration (minutes): 180 mins
    • Exam style: Multiple Choice
    • Open Book: No
     
    Incompany

    Are you a motivated data scientist and want to learn how to build and operate machine learning solutions in the cloud? Then this training is for you!

    Training code
    CGADP100CE
    Spoken Language
    English
    Language Materials
    English
    Dayparts
    6
    Price
    €2.000,00
    excl. VAT No extra costs.

    With an Incompany training you have several advantages:

    - You choose the location
    - You experience the training with your colleagues, so it is always in line with your practice
    - The trainer can tailor explanations, examples and assignments to your organization
    - In consultation exercises can be adapted to organization-specific questions

    Request more information or a quote.

     

    What is DP-100: Designing and Implementing a Data Science Solution on Azure (DP-100T01-A)

    Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

     
     

    Who should attend DP-100: Designing and Implementing a Data Science Solution on Azure (DP-100T01-A)

    This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

    Prerequisites

    Before attending this course, students must have:
    A fundamental knowledge of Microsoft Azure

    • Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
    • Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.

    Objectives

    At the end of the training, you will be able to:

    • Provision an Azure Machine Learning workspace
    • Use tools and code to work with Azure Machine Learning
    • Use designer to train a machine learning model
    • Deploy a designer pipeline as a service
    • Run code-based experiments in an Azure Machine Learning workspace
    • Train and register machine learning models
    • Create and consume Datastores
    • Create and consume Datasets
    • Create and use environments
    • Create and use compute targets
    • Create pipelines to automate machine learning workflows
    • Publish and run pipeline services
    • Publish a model as a real-time inference service
    • Publish a model as a batch inference service
    • Optimize hyperparameters for model training
    • Use automated machine learning to find the optimal model for your data
    • Generate model explanations with automated machine learning
    • Use explainers to interpret machine learning models
    • Use application insights to monitor a published model
    • Monitor data drift

    Exam information


    • Exam duration (minutes): 180 mins
    • Exam style: Multiple Choice
    • Open Book: No
     

  • Related

    Fields of Expertise
    Data
     

  • e-CF competences with this course

     

    At Capgemini Academy we believe in transparency and clarity in the training landscape. That is why, in the table below, we show you to which e-CF competence this training or certification contributes. For more information about how to use the e-Competence Framework read more here. If you want to know how you can apply the e-CF within your organization, read more on this page.

    e-Competence Level12345
    A.5.Architecture Design     
    B.6.ICT System Engineering     
    C.5.Systems Management     
    B.1.Application Development