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

  • 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
    €1.700,00
    excl. VAT No extra costs.

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

    This course will mostly take place in a group setting. We use several learning methods to help you obtain the knowledge, give you helpful insights and get you inspired. Check the Spoken Language 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) including exam voucher

    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.
    Course outline
    Module 1: Introduction to Azure Machine Learning
    In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.
    After completing this module, you will be able to

    • Provision an Azure Machine Learning workspace
    • Use tools and code to work with Azure Machine Learning

    Module 2: No-Code Machine Learning with Designer
    This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.
    After completing this module, you will be able to

    • Use designer to train a machine learning model
    • Deploy a Designer pipeline as a service

    Module 3: Running Experiments and Training Models
    In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.
    After completing this module, you will be able to

    • Run code-based experiments in an Azure Machine Learning workspace
    • Train and register machine learning models

    Module 4: Working with Data
    Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
    After completing this module, you will be able to

    • Create and consume datastores
    • Create and consume datasets

    Module 5: Compute Contexts
    One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.
    After completing this module, you will be able to

    • Create and use environments
    • Create and use compute targets

    Module 6: Orchestrating Operations with Pipelines
    Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module.
    After completing this module, you will be able to

    • Create pipelines to automate machine learning workflows
    • Publish and run pipeline services

    Module 7: Deploying and Consuming Models
    Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.
    After completing this module, you will be able to

    • Publish a model as a real-time inference service
    • Publish a model as a batch inference service

    Module 8: Training Optimal Models
    By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.
    After completing this module, you will be able to

    • Optimize hyperparameters for model training
    • Use automated machine learning to find the optimal model for your data

    Module 9: Interpreting Models
    Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.
    After completing this module, you will be able to

    • Generate model explanations with automated machine learning
    • Use explainers to interpret machine learning models

    Module 10: Monitoring Models
    After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.
    After completing this module, you will be able to

    • Use Application Insights to monitor a published model
    • Monitor data drift
     

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

    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
     
    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
    €1.700,00
    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) including exam voucher

    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.
    Course outline
    Module 1: Introduction to Azure Machine Learning
    In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.
    After completing this module, you will be able to

    • Provision an Azure Machine Learning workspace
    • Use tools and code to work with Azure Machine Learning

    Module 2: No-Code Machine Learning with Designer
    This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.
    After completing this module, you will be able to

    • Use designer to train a machine learning model
    • Deploy a Designer pipeline as a service

    Module 3: Running Experiments and Training Models
    In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.
    After completing this module, you will be able to

    • Run code-based experiments in an Azure Machine Learning workspace
    • Train and register machine learning models

    Module 4: Working with Data
    Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
    After completing this module, you will be able to

    • Create and consume datastores
    • Create and consume datasets

    Module 5: Compute Contexts
    One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.
    After completing this module, you will be able to

    • Create and use environments
    • Create and use compute targets

    Module 6: Orchestrating Operations with Pipelines
    Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module.
    After completing this module, you will be able to

    • Create pipelines to automate machine learning workflows
    • Publish and run pipeline services

    Module 7: Deploying and Consuming Models
    Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.
    After completing this module, you will be able to

    • Publish a model as a real-time inference service
    • Publish a model as a batch inference service

    Module 8: Training Optimal Models
    By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.
    After completing this module, you will be able to

    • Optimize hyperparameters for model training
    • Use automated machine learning to find the optimal model for your data

    Module 9: Interpreting Models
    Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.
    After completing this module, you will be able to

    • Generate model explanations with automated machine learning
    • Use explainers to interpret machine learning models

    Module 10: Monitoring Models
    After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.
    After completing this module, you will be able to

    • Use Application Insights to monitor a published model
    • Monitor data drift
     

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

    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
     
  • Related

    Fields of Expertise
    Cloud
     
  • 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.Systems Engineering     
    B.1.Application Development