A data scientist is creating a responsive model that will update a product's daily pricing based on the previous day's sales volume. Which of the following resource constraints is the data scientist's greatest concern?

The model must be retrained every 24 hours so that today’s pricing reflects yesterday’s sales. Because this retraining occurs on a fixed, short schedule, the dominant constraint is how long it takes to complete each training run; excessive training time would delay or prevent daily price updates. The other activities (initial development, deployment automation, and ingesting the previous day’s sales, which are already stored) occur once or require far fewer resources than the recurrent training step.

A. Deployment time – Once a CI/CD pipeline is built, pushing the new model is fast and usually automated; it runs after training completes.
C. Development time – Model design and coding are mostly one-off efforts completed before daily operations begin.
D. Data collection time – Yesterday’s sales data already reside in transactional systems; extraction is trivial compared with compute-intensive retraining.

1. Google Cloud, “MLOps: Continuous Delivery and Automation Pipelines in Machine Learning,” v2, 2020, p.4 §“Training frequency and compute cost”.

2. AWS, “Machine Learning Operations (MLOps) Fundamentals,” whitepaper, 2021, p.9 §“Automated Model Retraining and Scheduling”.

3. MIT OpenCourseWare, 6.S897 “Machine Learning Production Systems,” Lecture 4 slides, slide 12 “Time-boxed retraining as the main operational bottleneck”.

4. Stanford CS329S “Machine Learning Systems Design,” Spring 2022 notes, Week 5 §“Retraining cadence and resource constraints”.