Answer-first summary for fast verification
Answer: AWS Lambda, consider using AWS SageMaker for building and deploying machine learning models, and invoking the models from Lambda functions.
Option C is the most appropriate choice for this scenario. AWS Lambda is a serverless compute service that can be used to invoke machine learning models built and deployed using AWS SageMaker. By using SageMaker, you can leverage a wide range of built-in algorithms and bring your own algorithms to build, train, and deploy machine learning models. When integrating machine learning models into the data transformation process, consider factors such as data preprocessing, model versioning, model inference performance, and model monitoring. You should also consider the security and access control mechanisms for the machine learning models and their input/output data.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
Your company is planning to implement a data transformation service that can handle complex data processing tasks with a focus on machine learning integration. Which AWS service would you recommend, and what are the key considerations for integrating machine learning models into the data transformation process?
A
Amazon EMR, consider using Apache Spark MLlib for machine learning tasks and integrating the models with the data processing pipeline.
B
Amazon ECS, consider using containerized applications with machine learning frameworks like TensorFlow or PyTorch, and integrating the models with the data processing pipeline.
C
AWS Lambda, consider using AWS SageMaker for building and deploying machine learning models, and invoking the models from Lambda functions.
D
Amazon EKS, consider using Kubernetes operators for machine learning tasks and integrating the models with the data processing pipeline.
No comments yet.