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

Duration : 4 days

This Microsoft Certification Training explores the use of cloud-scale machine learning solutions with Azure Machine Learning. It enables you to leverage your knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, as well as monitoring machine learning solutions in Microsoft Azure.

This course provides comprehensive preparation for the DP-100 exam: Designing and Implementing a Data Science Solution on Azure, required to obtain the Microsoft Certified: Azure Data Scientist Associate certification.

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Getting Started with Cosmos DB NoSQL Development (DP-3015) - Training Courses | Afi U.

Developing Solutions for Microsoft Azure (AZ-204T00) - Training Courses | Afi U.

Audience

This training is designed for data scientists with existing knowledge of Python and machine learning frameworks such as Scikit-Learn, PyTorch, and TensorFlow, who wish to create and operate machine learning solutions in the cloud.

Prerequisites

• Proficiency in the Python programming language
• Familiarity with machine learning frameworks
• Understanding of machine learning concepts
• Basic knowledge of Azure

• Design a data ingestion strategy for machine learning projects
• Design a model training solution for machine learning
• Design a model deployment solution
• Explore Azure Machine Learning workspace resources and assets
• Explore development tools for interacting with the workspace
• Make data available in Azure Machine Learning
• Use compute targets in Azure Machine Learning
• Use environments in Azure Machine Learning
• Identify the best classification model using automated machine Learning

Contents

• Design a Data Ingestion Strategy for Machine Learning Projects

  • Identify the source and format of data.
  • Determine how to distribute data to machine learning workflows.
  • Design an efficient data ingestion solution.

  Design a Machine Learning Model Training Solution

  • Identify the machine learning tasks to be performed.
  • Select an appropriate service for model training.
  • Choose between different compute options.

  Design a Model Deployment Solution

  • Understand how the model will be consumed.
  • Decide between real-time or batch deployment options.

  Explore Azure Machine Learning Workspace Resources and Assets

  • Create an Azure Machine Learning workspace.
  • Identify the resources and assets available in Azure Machine Learning.
  • Train models within the workspace.

  Explore Development Tools for Workspace Interaction

  • Discover Azure Machine Learning Studio.
  • Explore the Python SDK.
  • Use the CLI interface.

  Make Data Available in Azure Machine Learning

  • Understand the use of URIs.
  • Create a datastore.
  • Configure a data resource.

  Use Compute Targets in Azure Machine Learning

  • Create and use a compute instance.
  • Configure and operate a compute cluster.

  Manage Environments in Azure Machine Learning

  • Understand the concept of environments.
  • Explore and use prebuilt environments.
  • Create and use custom environments.

  Identify the Best Classification Model with Automated Machine Learning

  • Preprocess data and configure features.
  • Run an automated machine learning experiment.
  • Evaluate and compare generated models.

  Track Model Training in Jupyter Notebooks with MLflow

  • Configure MLflow to track models in notebooks.
  • Train and track models directly from notebooks.

  Run a Training Script as a Command Job in Azure Machine Learning

  • Convert a notebook into an executable script.
  • Run a script as a command job.
  • Pass parameters to a command job.

  Track Model Training with MLflow in Azure Jobs

  • Track metrics with MLflow.
  • Visualize metrics and evaluate models.

  Execute Pipelines in Azure Machine Learning

  • Create and configure components.
  • Design a complete pipeline.
  • Run a pipeline as an automated job.

  Optimize Hyperparameters with Azure Machine Learning

  • Define a search space for hyperparameters.
  • Configure a sampling method.
  • Set up an early stopping mechanism.
  • Use a sweep job to optimize hyperparameters.

  Deploy a Model to a Managed Online Endpoint

  • Explore managed online endpoints.
  • Deploy an MLflow model to a managed online endpoint.
  • Test and validate online endpoints.

  Deploy a Model to a Batch Endpoint

  • Understand and configure batch endpoints.
  • Deploy an MLflow model to a batch endpoint.
  • Deploy a custom model to a batch endpoint.
  • Call batch endpoints and troubleshoot issues.