A data science team has finished cleaning and versioning their training dataset and wants to begin iterative model experimentation. Which lifecycle stage immediately follows data preparation, and what is the primary software concern at that stage?
- AModel registry - storing approved model artefacts for controlled promotion to production
- BInference serving - deploying a model endpoint so applications can request predictions in real time
- CTraining framework - orchestrating compute, defining model architecture, and running gradient-based optimisation on the prepared data Correct
- DMonitoring - tracking data drift and model performance degradation against production baselines
Why A is wrong: A model registry manages promotion of validated models, not the iterative experimentation that comes directly after data preparation. Registries operate after training and evaluation are complete.
Why B is wrong: Inference serving comes after a model is trained and registered. Running a serving layer on unprepared, untrained weights produces no useful predictions and is the wrong stage.
Why C is correct: After data is prepared and versioned the next stage is model training, where a framework such as PyTorch or TensorFlow defines the network, manages distributed compute, and executes the optimisation loop.
Why D is wrong: Monitoring is a post-deployment concern that tracks live model behaviour. There is no production model to monitor immediately after data preparation, so this stage does not follow data prep.