Designing and Implementing a Data Science Solution on Azure | DP-100


Microsoft Azure is a set of cloud computing services that constantly continues to grow. Azure helps your organization solve all kinds of business challenges. With Azure, your organization has the freedom to use your favourite tools and frameworks to develop, manage, and implement applications on a large, global network.

During the course Designing and Implementing a Data Science Solution on Azure (DP-100) you 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.

This course is focused on Azure and does not teach the student how to do data science. It is assumed students already know that.

The course helps to prepare for exam DP-100.

Lunches and course materials are included. Exam fees are not included.

Deze training wordt in het Nederlands verzorgd.

Doelgroep

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.

Voorkennis

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.

Duur

The course lasts three days.

Groepsgrootte

The size of the group is a maximum of twelve candidates.

Certificaat

After the course you can participate in the exam. The course helps to prepare for exam DP-100. The exam fees are not included.

Module 1: Introduction to Azure Machine Learning

  • Getting Started with Azure Machine Learning
  • Azure Machine Learning Tools

Module 2: No-Code Machine Learning with Designer

  • Training Models with Designer
  • Publishing Models with Designer

Module 3: Running Experiments and Training Models

  • Introduction to Experiments
  • Training and Registering Models

Module 4: Working with Data

  • Working with Datastores
  • Working with Datasets

Module 5: Compute Contexts

  • Working with Environments
  • Working with Compute Targets

Module 6: Orchestrating Operations with Pipelines

  • Introduction to Pipelines
  • Publishing and Running Pipelines

Module 7: Deploying and Consuming Models

  • Real-time Inferencing
  • Batch Inferencing

Module 8: Training Optimal Models

  • Hyperparameter Tuning
  • Automated Machine Learning

Module 9: Interpreting Models

  • Introduction to Model Interpretation
  • Using Model Explainers

Module 10: Monitoring Models

  • Monitoring Models with Application Insights
  • Monitoring Data Drift
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Virtual Classroom 4 dagen 16 t/m 19 februari 2026

16 februari 2026 10:30 - 18:00 uur
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Virtual Classroom 4 dagen 27 t/m 30 april 2026

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Virtual Classroom 4 dagen 1 t/m 4 september 2026

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Virtual Classroom 4 dagen 23 t/m 26 november 2026

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