Memory hook: Simplest suitable model, measured on the task.
Must remember
- Use BigQuery ML when SQL-centric teams can train and infer near warehouse data; AutoML when supported managed model search meets the task; custom training when architecture or training control demands it.
- Classification predicts categories, regression numeric outcomes, forecasting time-dependent values and clustering unlabeled structure. An interpretable baseline helps expose whether extra complexity is worthwhile.
- Pretrained APIs such as Document AI, Vision and Translation solve established capabilities. Model Garden supplies model options; compare Gemini, image/video models and open models by modality, quality, licensing and deployment needs.
- For generative AI, begin with prompting and grounded retrieval. Fine-tuning can change behavior or domain performance, but it is not the simplest answer to frequently changing facts.
- Evaluate cost per useful result, latency, throughput, privacy, context size and regional availability. A managed model API shifts operations but retains quotas, identity and evaluation responsibilities.
- BigQuery supports ML prediction and supported remote model integration, including supported tuning/inference workflows. Verify the model-specific API rather than assuming every family accepts the same operations.
Choose under exam pressure
| Requirement | Choice and reason |
|---|---|
| A SQL analyst needs a tabular baseline | BigQuery ML before building custom distributed infrastructure. |
| An existing document extraction API meets requirements | Use and evaluate that API before training from scratch. |
Traps
- A foundation model benchmark does not establish quality on your own task.
- The most complex model is not automatically the most interpretable or cost-effective.
Active recall
1. When choose custom training?
When required architecture, framework or training behavior exceeds managed options.
2. Why start with a baseline?
It provides a measurable reference for accuracy, cost and complexity.
3. What can fine-tuning improve?
Task behavior, style or specialized performance when suitable training data and evaluation exist.
4. Why compare model deployment options?
Privacy, latency, hardware, availability and operational control differ.
5. What is the key model-selection evidence?
Representative evaluation results against the business acceptance criteria.