Gain the necessary knowledge about how to use Azure services to develop, train, and deploy, machine learning solutions. The course starts with an overview of Azure services that support data science. From there, it focuses on using Azure's premier data science service, Azure Machine Learning service, to automate the data science pipeline. This course is focused on Azure and does not teach the student how to do data science. It is assumed students already know that.
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Description
- Set up an Azure Machine Learning workspace (30-35%)
- Run experiments and train models (25-30%)
- Optimize and manage models (20-25%)
- Deploy and consume models (20-25%)
Audience
As a candidate for this exam, you should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. Additionally, you should have knowledge of optimizing language models for AI applications using Azure AI.
Your responsibilities for this role include:
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Designing and creating a suitable working environment for data science workloads.
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Exploring data.
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Training machine learning models.
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Implementing pipelines.
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Running jobs to prepare for production.
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Managing, deploying, and monitoring scalable machine learning solutions.
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Using language models for building AI applications.
As a candidate for this exam, you should have knowledge and experience in data science by using:
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Azure Machine Learning
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MLflow
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Azure AI services, including Azure AI Search
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Azure AI Foundry