QuestionQ206
Deployment StrategiesA Data Scientist is developing a propensity model for an e-commerce start-up. The company stores 7 GB of historical data and receives approximately 5 MB of new transaction data each day. The objective is to produce daily purchase predictions for every user by 7:00 AM each morning. Because the start-up is still in its early stages, the Data Scientist must emphasize a highly cost-efficient solution.
Which approach should the Data Scientist use?
- A Use a multi-node compute and leverage distributed frameworks like SparkML to train the model. The model can then be scheduled as a nightly batch job that runs on a multi-node compute.
- B Use a single-node memory compute to build a model with libraries like scikit-learn. The model can then be scheduled as a nightly batch job that runs on a single-node compute.
- C Use a single-node compute to build a model with libraries like scikit-learn. The model can then be deployed as an always-on REST API so that users can query the API to get the predictions whenever they want.
- D Use a single-node compute to build a model with libraries like scikit-learn. The model can then be integrated into an always-on streaming pipeline, to ensure immediate processing of incoming data in order to meet the SLA.
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