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Answer: Implement a system for tracking and logging model predictions and actual outcomes for continuous evaluation., Establish a protocol for automatic retraining of the model upon detection of significant performance degradation.
Implementing a system for tracking and logging model predictions (B) and establishing a protocol for automatic retraining (C) are critical for ensuring the model remains effective over time in a production environment. These measures address real-time performance tracking, compliance, and the ability to detect and respond to data drift. While tuning hyperparameters (A) and documenting the model's decision-making process (D) are important, they do not directly contribute to the ongoing monitoring and maintenance of the model's performance in production. Option E combines B and C, which are both essential for a comprehensive approach to production readiness.
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As the lead of a machine learning team preparing to launch a new project, you're tasked with assessing the production readiness of the ML components. The team has already evaluated features and data, model development, and infrastructure. Considering the need for real-time performance tracking, compliance with industry standards, and the ability to detect and respond to data drift, what additional readiness checks should you advise the team to perform? (Choose two options)
A
Ensure that all hyperparameters are tuned to their optimal values.
B
Implement a system for tracking and logging model predictions and actual outcomes for continuous evaluation.
C
Establish a protocol for automatic retraining of the model upon detection of significant performance degradation.
D
Document the model's decision-making process for transparency and audit purposes.
E
Both B and C are necessary for comprehensive production readiness.
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