APPLICATION OF SUPERVISED LEARNING
MACHINE LEARNING PIPELINE
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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You are tasked to answer whether a company should email a particular customer based on past customer responses to a marketing campaign. You will create this machine learning model using XGBoost.What Amazon SageMaker option should the company use to train their ML models that reduces the management and automates the pipeline for future retraining?
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Create and train your XGBoost algorithm on your local laptop and then use an Amazon SageMaker endpoint to host the ML model.
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Use Amazon in-built algorithms to run the training and inference jobs.
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Use the Build Your Own Container (BYOC) Amazon SageMaker option. Create a new Docker container with the existing code. Register the container in Amazon Elastic Container Registry (ECR). Finally, run the training and inference jobs using this container.
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Create a new Amazon SageMaker notebook instance. Copy the existing code into an Amazon SageMaker notebook. Then, run the pipeline from this notebook.
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Explanation:
Detailed explanation-1: -A machine learning pipeline is the end-to-end construct that orchestrates the flow of data into, and output from, a machine learning model (or set of multiple models). It includes raw data input, features, outputs, the machine learning model and model parameters, and prediction outputs.
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