A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML…

The core requirements are predicting customer demand without any coding experience or knowledge of machine learning (ML) algorithms. Amazon SageMaker Canvas is a visual, no-code service designed specifically for business users to generate ML predictions without writing code. It allows users to import datasets, select a target column for prediction (in this case, demand), and automatically builds, trains, and explains the model. Its support for time-series forecasting makes it the ideal solution for this demand prediction use case, fulfilling all the company's requirements.

1. Amazon SageMaker Canvas Developer Guide: "Amazon SageMaker Canvas is a visual

point-and-click service that enables business analysts to generate accurate ML predictions on their own — without requiring any machine learning experience or having to write a single line of code." (Source: AWS Documentation

Amazon SageMaker Canvas Developer Guide

"What Is Amazon SageMaker Canvas?")

2. Amazon SageMaker Canvas Developer Guide

Time series forecasting: "With time series forecasting

you can make predictions for a single item over time... For example

you might want to forecast the future sales of a single product in your store." This directly matches the use case of predicting demand for memory hardware. (Source: AWS Documentation

Amazon SageMaker Canvas Developer Guide

"Building a model

" "Time series forecasting.")

3. Amazon SageMaker Developer Guide

Use Amazon SageMaker Built-in Algorithms: The documentation consistently shows examples of using the SageMaker Python SDK within a Jupyter Notebook to instantiate

train

and deploy models using built-in algorithms

demonstrating the requirement for code. (Source: AWS Documentation

Amazon SageMaker Developer Guide

"Use Amazon SageMaker Built-in Algorithms or Pre-trained Models.")

4. Amazon Personalize Developer Guide: "Amazon Personalize is a machine learning service that makes it easy for developers to create individualized recommendations for customers using their applications." This confirms its purpose is recommendations

not forecasting. (Source: AWS Documentation

Amazon Personalize Developer Guide

"What Is Amazon Personalize?")