In an AI life cycle, what comes straight after data preparation?
Why D is the answer
Model training
Prepared data feeds training, then come validation and deployment.
In detail
- Prepared data feeds training.
- Data comes first.
Use case
An illustrative scenario, not a real incident.
Once data is prepared, training begins, followed by validation and deployment.
See it in different ways
flowchart TD A["Training"] --> B["Large clusters, fast networks, long jobs"] C["Inference"] --> D["Low latency, many requests, scaling with demand"]
mindmap
root((AI))
Machine learning
Deep learning
Generative AI
Drivers
GPUs
Data
Algorithms
Stages
Training
Inferenceflowchart LR S0["Data preparation"] --> S1["Model training"] --> S2["Validation"] --> S3["Deployment"]
Key point: Training follows data preparation.
Why the other options are wrong
- A. Production inference
- That comes later.
- B. Retiring the model
- That comes last.
- C. Ongoing monitoring
- That comes after deployment.