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

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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 Information:

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

Exam guarantee:
We have full confidence in the quality of our training. Therefore, if you take this training in our open schedule, we offer an exam guarantee. This means that you can retake the training for free, and you'll receive a complimentary exam voucher if you don't pass the exam on your first attempt.
The following conditions apply:

• You attended the entire training.
• You took the first exam within 2 months after the training.
• There is a maximum of 1 year between your initial training and the free training.

254934

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 Information:

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

Exam guarantee:
We have full confidence in the quality of our training. Therefore, if you take this training in our open schedule, we offer an exam guarantee. This means that you can retake the training for free, and you'll receive a complimentary exam voucher if you don't pass the exam on your first attempt.
The following conditions apply:

• You attended the entire training.
• You took the first exam within 2 months after the training.
• There is a maximum of 1 year between your initial training and the free training.