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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.
Passing the Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure exam is the major requirement for earning the Microsoft Certified: Azure Data Scientist Associate certification. This test measures the ability of the professionals to execute the following technical tasks: setting up an Azure Machine Learning workspace; running experiments & train models; optimizing and handling models; deploying and consuming models.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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Microsoft DP-100日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Topic 2: Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Plan and configure Azure Machine Learning workspace - Manage compute and data assets |
| Topic 3: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Topic 4: Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Topic 5: Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
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