Learn the essential steps for operationalizing machine learning projects with Azure Machine Learning. Discover how to automate ML pipelines, trigger workflows via HTTPS requests, and implement best practices for efficient deployment.
Table of Contents
Question
When a machine learning project is being prepared for operationalization, which of the following steps should be done using Azure Machine Learning?
A. Work automation using an ML pipeline and triggered using an HTTPS request
B. Running a clustering algorithm on a verification dataset
C. Manual model deployment using Python and REST APIs
D. Changing regularization terms for each ML model before deployment
Answer
A. Work automation using an ML pipeline and triggered using an HTTPS request
Explanation
Work automation using an ML pipeline and triggered using an HTTPS request is directly supported by Azure Machine Learning features such as ML pipelines and managed endpoints. This allows for the automated deployment, scoring, and monitoring of models which is crucial for operationalization. These pipelines can be triggered by various events, including HTTPS requests.
Manual model deployment using Python and REST APIs is not a recommended method for operationalization. While Azure Machine Learning supports deployment using Python scripts, manual deployment is less preferred for operationalization due to scalability and maintainability concerns. Automated pipelines are generally recommended.
Running a clustering algorithm on a verification dataset is not an operationalization step. This is typically done during model development and evaluation, not operationalization. Azure Machine Learning can support running clustering algorithms, but it falls outside the scope of operationalization in this context.
Changing regularization terms for each ML model before deployment is not directly related to operationalization. This step is part of fine-tuning the model and typically falls under model development and optimization. While Azure Machine Learning can be used for model training and hyperparameter tuning (which includes regularization terms), it is not directly related to operationalization.
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.