DP-100

Azure data scientist associate certification and knowledge of data science workflow and principal

Candidates can achieve this certification by passing the following exam(s).

• Exam DP-100 – Designing and Implementing a Data Science Solution

The certification exam can be registered and attempted within 3 months of course/module completion at Logitrain training centre on weekdays during normal business hours (excludes public holidays)

• Create an Azure Machine Learning workspace
• Manage data objects in an Azure Machine Learning workspace
• Manage experiment compute contexts
• Create models by using Azure Machine Learning designer
• Run training scripts in an Azure Machine Learning workspace
• Generate metrics from an experiment run
• Automate the model training process
• Use Automated ML to create optimal models
• Use Hyperdrive to tune hyperparameters
• Manage models
• Use model explainers to interpret models
• Create a pipeline for batch inferencing
• Publish a pipeline as a web service
• Deploy a model as a service
• Create production compute targets

This course is likely to add to the employment-related skills of the participants. The skills developed are likely to be used in the course of being an employee or working in a business.

• IT professionals who know data science and workloads
• Azure data scientist

• Create an Azure Machine Learning workspace
• Configure workspace settings
• Manage a workspace by using Azure Machine Learning Studio
• Register and maintain data stores
• Create and manage datasets
• Create a compute instance
• Determine appropriate compute specifications for a training workload
• Create compute targets for experiments and training
• 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 the designer
• 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
• Log metrics from an experiment run
• Retrieve and view experiment outputs
• Use logs to troubleshoot experiment run errors
• Create a pipeline by using the SDK
• Pass data between steps in a pipeline
• Run a pipeline
• Monitor pipeline runs
• 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
• Select a sampling method
• Define the search space
• Define the primary metric
• Define early termination options
• Find the model that has optimal hyperparameter values
• Select a model interpreter
• Generate feature importance data
• Register a trained model
• Monitor model history
• Monitor data drift
• Consider security for deployed services
• Evaluate compute options for the deployment
• Configure deployment settings
• Consume a deployed service
• Troubleshoot deployment container issues
• Publish a batch inferencing pipeline
• Run a batch inferencing pipeline and obtain outputs
• Create a target compute resource
• Configure an Inference pipeline
• Consume a deployed endpoint