Knowledge Graph — Coursera Notes › Academic disciplines › Information Technology / Computer Science
AI/ML Engineer
concept · part of Information Technology / Computer Science
An AI/ML engineer is responsible for the entire model life cycle, from data collection and preprocessing to model development, evaluation, deployment, and ongoing maintenance. They ensure models are scalable, reliable, and perform well in production. Key tasks include handling data quality, selecting algorithms, optimizing hyperparameters, deploying on cloud platforms, and monitoring for model drift. Collaboration with data scientists, software developers, and business teams is essential to align technical solutions with business goals.
Inside AI/ML Engineer (5)
- Ongoing monitoring and maintenance — AI/ML models require continuous monitoring after deployment to maintain performance.
- Collaboration — AI/ML engineers collaborate with data scientists to experiment with algorithms and derive insights, with software developers to integrate models into production software, and with business teams to communicate model impact and align with business goals.
- Data collection and preprocessing — AI/ML engineers gather large datasets from various sources and clean, preprocess, and structure the data for ML models.
- Model evaluation and optimization — After development, AI/ML engineers evaluate model performance using metrics like accuracy, precision, recall, F1-score for classification, and mean squared error (MSE) or R-squared for regression.
- Model life cycle — The model life cycle encompasses all stages from data collection, preprocessing, model development, evaluation, deployment, to ongoing monitoring and maintenance.
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