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
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