Azure Data Scientist Associate (DP-100)

This learning path is designed to help you prepare for Microsoft’s DP-100 Designing and Implementing a Data Science Solution on Azure exam. Even if you don’t plan to take the exam, these courses and hands-on labs will help you learn how to use Azure’s machine learning solutions.
Candidates who pass the DP-100 exam will earn the Microsoft Certified: Azure Data Scientist Associate certification.

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This learning path is designed to help you prepare for Microsoft's DP-100 Designing and Implementing a Data Science Solution on Azure exam. Even if you don't plan to take the exam, these courses and hands-on labs will help you learn how to use Azure's machine learning solutions.
Candidates who pass the DP-100 exam will earn the Microsoft Certified: Azure Data Scientist Associate certification.

Course content

 

Set up an Azure Machine Learning Workspace

 

Create an Azure Machine Learning workspace 
  • create an Azure Machine Learning workspace
  • configure workspace settings
  • manage a workspace by using Azure Machine Learning studio
Manage data objects in an Azure Machine Learning workspace
  • register and maintain data stores
  • create and manage datasets
Manage experiment compute contexts
  • create a compute instance
  • determine appropriate compute specifications for a training workload
  • create compute targets for experiments and training

 

Run Experiments and Train Models

 

Create models by using Azure Machine Learning Designer 
  • create a training pipeline by using Azure Machine Learning designer
  • ingest data in a designer pipeline
  • use designer modules to define a pipeline data flow
  • use custom code modules in designer
Run training scripts in an Azure Machine Learning workspace
  • create and run an experiment by using the Azure Machine Learning SDK
  • consume data from a data store in an experiment by using the Azure Machine Learning SDK
  • consume data from a dataset in an experiment by using the Azure Machine Learning SDK
  • choose an estimator for a training experiment
Generate metrics from an experiment run
  • log metrics from an experiment run
  • retrieve and view experiment outputs
  • use logs to troubleshoot experiment run errors
Automate the model training process
  • create a pipeline by using the SDK
  • pass data between steps in a pipeline
  • run a pipeline
  • monitor pipeline runs

 

Optimize and Manage Models

 

Use Automated ML to create optimal models
  • use the Automated ML interface in Azure Machine Learning studio
  • use Automated ML from the Azure Machine Learning SDK
  • select scaling functions and pre-processing options
  • determine algorithms to be searched
  • define a primary metric
  • get data for an Automated ML run
  • retrieve the best model
Use Hyperdrive to tune hyperparameters
  • select a sampling method
  • define the search space
  • define the primary metric
  • define early termination options
  • find the model that has optimal hyperparameter values
Use model explainers to interpret models
  • select a model interpreter
  • generate feature importance data
Manage models 
  • register a trained model
  • monitor model history
  • monitor data drift

 

Deploy and Consume Models

 

Create production compute targets
  • consider security for deployed services
  • evaluate compute options for deployment
Deploy a model as a service
  • configure deployment settings
  • consume a deployed service
  • troubleshoot deployment container issues
Create a pipeline for batch inferencing 
  • publish a batch inferencing pipeline
  • run a batch inferencing pipeline and obtain outputs
Publish a designer pipeline as a web service
  • create a target compute resource
  • configure an Inference pipeline
  • consume a deployed endpoint

 

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Course Prerequisites

 
  • Basic experience using Microsoft Azure

Who can attend

 
  • The Azure Data Scientist applies their knowledge of data science and machine learning to implementing and running machine learning workloads on Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.

Number of Hours: 40hrs

Certification

DP-100

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  • Virtual Live Experience
  • Preparing for Certification

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