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3. Models Management and Optimization (20-25%):
- Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
- Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
- Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.
- Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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4. Models Deployment and Consumption (20-25%):
- Designer pipeline publishing as a web service: The candidates should show their knowledge of target compute resources creation, inference pipelines configuration, and deployed endpoints consuming.
- Model-as-a-Service deployment: This subtopic will measure the individuals’ expertise in configuring deployment settings, troubleshooting issues with deployment containers, and consuming deployed services.
- Creation of pipelines for batch inferencing: This subject area covers your competence in running batch inferencing pipelines and obtaining outputs as well as publishing batch inferencing pipelines.
- Production computes targets creation: The test takers should perform their skills in compute options evaluation for deployment and consideration of security for deployed services.
Microsoft DP-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Prepare a model for deployment | 20-25% | - Manage deployment assets
|
| Explore data and train models | 35-40% | - Prepare data for modeling
|
| Design and prepare a machine learning solution | 20-25% | - Design an Azure Machine Learning workspace
|
| Deploy and retrain models | 10-15% | - Monitor deployed models
|
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