Which solution will meet this requirement in the MOST operationally efficient way?
Use the Amazon SageMaker DeepAR forecasting algorithm to build a single model for all the products.
Use the Amazon SageMaker DeepAR forecasting algorithm to build separate models for each product.
Use Amazon SageMaker Autopilot to build a single model for all the products.
Use Amazon SageMaker Autopilot to build separate models for each product.
Explanations:
The DeepAR algorithm in Amazon SageMaker is designed for time series forecasting and can handle multiple time series within a single model, making it operationally efficient to forecast sales for all 100 rod types together. This reduces the overhead of managing multiple models.
Building separate models for each product using DeepAR would be inefficient, requiring more resources and management. Since the products share similar forecasting characteristics, a single model would be more operationally efficient.
Amazon SageMaker Autopilot is primarily used for tabular data and automatic machine learning (AutoML). It is not ideal for time series forecasting, making it less suitable for this specific task of predicting sales of steel rods.
Similar to option C, using Amazon SageMaker Autopilot for separate models is not a good fit for time series forecasting. Autopilot isn’t designed to handle the complexities of time series data as well as DeepAR, making it less efficient for this use case.
Based on what I know, the answer is:
Use the Amazon SageMaker DeepAR forecasting algorithm to build a single model for all the products.