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Ongoing monitoring and maintenance

feature · part of AI/ML Engineer

AI/ML models require continuous monitoring after deployment to maintain performance. Engineers detect model drift—where models become less accurate as data evolves—and retrain models when necessary. They set up monitoring tools and dashboards to track key metrics and alert teams to issues. This ensures models remain reliable and relevant over time.

Inside Ongoing monitoring and maintenance (2)

• Model drift — Model drift is the phenomenon where ML models become less accurate over time as the data they process evolves.
• Performance monitoring — Engineers set up monitoring tools and dashboards to track key performance metrics of deployed models, alerting teams if performance degrades.

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