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Answer: Collect diverse bank statements, train model using supervised learning, test using cross-validation, deploy via Azure portal, monitor using Azure Monitor.
Diverse data collection ensures model robustness, supervised learning is suitable for labeled data, cross-validation provides reliable testing, deployment via Azure portal is straightforward, and Azure Monitor offers comprehensive monitoring capabilities.
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Given a scenario where you need to train, test, and publish a custom document intelligence model to extract financial data from bank statements, describe the detailed steps you would take, including data collection, model training, testing methodologies, and deployment strategies. Additionally, explain how you would monitor the model's performance post-deployment.
A
Collect diverse bank statements, train model using supervised learning, test using cross-validation, deploy via Azure portal, monitor using Azure Monitor.
B
Collect standardized bank statements, train model using unsupervised learning, test using hold-out method, deploy via Azure CLI, monitor using custom logging.
C
Collect varied bank statements, train model using semi-supervised learning, test using A/B testing, deploy via Azure SDK, monitor using Application Insights.
D
Collect uniform bank statements, train model using reinforcement learning, test using time-series analysis, deploy via Azure PowerShell, monitor using Log Analytics.
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