DP-100: Designing and Implementing a Data Science Solution on Azure Training Logo

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

Live Online & Classroom Enterprise Certification Training

In order to incorporate and operate machine learning workloads on Azure, the Azure Data Scientist applies his knowledge of data science and machine learning, in particular using the 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.

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What is DP-100: Designing and Implementing a Data Science Solution on Azure Course about?

In order to incorporate and operate machine learning workloads on Azure, the Azure Data Scientist applies his knowledge of data science and machine learning, in particular using the 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.

What are the objectives of DP-100: Designing and Implementing a Data Science Solution on Azure Course ?

●       Explain the concept of Azure Machine Learning

●       Discuss No-Code Machine Learning with Designer

●       Explain Running Experiments and Training Models

●       Discuss Working with Data

●       Describe Compute Contexts

●       Discuss Orchestrating Operations with Pipelines

●       Describe Deploying and Consuming Models

●       Explain Training Optimal Models

●       Describe Interpreting Models

●       Explain Monitoring Models

Who is DP-100: Designing and Implementing a Data Science Solution on Azure Course for?

●       Students or professionals who have successfully completed the training of Home / Microsoft / Designing and Implementing a Data Science Solution on Azure (DP-100T01) Designing and Implementing a Data Science Solution on Azure

●       Data scientists with existing knowledge of Python and machine learning frameworks

What are the prerequisites for DP-100: Designing and Implementing a Data Science Solution on Azure Course?

Fundamental knowledge of Microsoft Azure

Available Training Modes

Self-Paced Training

10 Hours

Course Outline Expand All

Expand All

  • Machine Learning with Azure
  • Getting Started with Azure Machine Learning
  • Azure Machine Learning Tools Overview
  • Azure Machine Learning Workspaces
  • 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
  • Azure Machine Learning datasets
  • Manage experiment compute contexts
  • Create a compute instance
  • Determine appropriate compute specifications for a training workload
  • Create compute targets for experiments and training
  • Create a Pipeline by using the SDK
  • Azure Machine Learning Pipelines
  • Setting up Environment
  • Run a pipeline
  • Pass data between steps in a pipeline
  • Pipeline Data object overview
  • PipelineData steps inputs and outputs
  • Reuse pipeline steps
  • Run a Pipeline
  • What is Pipeline Run
  • Process of Pipeline Run
  • Requesting an Agent
  • Report & Collect Results
  • Creating a pipeline for Batch Inferencing
  • Create and publish a batch inference pipeline
  • Consume a pipeline endpoint
  • Training Models with Designer
  • Models using Azure Machine Learning Designer
  • Training pipeline using Azure Machine Learning designer
  • Ingest data in a designer pipeline
  • Pipeline data flow with Designer modules
  • Working with Azure Machine Learning SDK
  • 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
  • Automated ML to create optimal models
  • Automated ML interface in Azure Machine Learning studio
  • Automated ML from the Azure Machine Learning SDK
  • Scaling functions and pre-processing options
  • Algorithms Search
  • Primary Metrics
  • Retrieving data for an Automated ML run
  • Retrieving the best model
  • Use Hyperdrive to tune hyper parameters
  • Select a sampling method
  • Define the search space
  • Define the primary metric
  • Define early termination options
  • Find the model that has optimal hyper parameter values
  • Use model explainers to interpret models
  • Select a model interpreter
  • Generate feature importance data
  • Manage models
  • Register a trained model
  • Monitor Models with Application Insights
  • Monitor model history
  • Data Drift Monitoring for Azure ML Datasets
  • 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
  • Publish a designer pipeline as a web service
  • Configure an Inference pipeline
  • Deploy user model

Who is the instructor for this training?

The trainer for this DP-100: Designing and Implementing a Data Science Solution on Azure Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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