Given these requirements, which of the following options is the MOST SUITABLE for orchestrating your ML workflow?

You are a machine learning engineer at a healthcare company responsible for developing and deploying an end-to-end ML workflow for predicting patient readmission rates. The workflow involves data preprocessing, model training, hyperparameter tuning, and deployment. Additionally, the solution must support regular retraining of the model as new data becomes available, with minimal manual intervention. You need to select the right solution to orchestrate this workflow efficiently while ensuring scalability, reliability, and ease of management.

Given these requirements, which of the following options is the MOST SUITABLE for orchestrating your ML workflow?
A . Implement the entire ML workflow using Amazon SageMaker Pipelines, which provides integrated orchestration for data processing, model training, tuning, and deployment
B . Use AWS Step Functions to define and orchestrate each step of the ML workflow, integrate with SageMaker for model training and deployment, and leverage AWS Lambda for data preprocessing tasks
C . Leverage Amazon EC2 instances to manually execute each step of the ML workflow, use Amazon RDS for storing intermediate results, and deploy the model using Amazon SageMaker endpoints
D . Use AWS Glue for data preprocessing, Amazon SageMaker for model training and tuning, and
manually deploy the model to an Amazon EC2 instance for inference

View Answer

Answer: A

Explanation:

Correct option:

Implement the entire ML workflow using Amazon SageMaker Pipelines, which provides integrated orchestration for data processing, model training, tuning, and deployment

Amazon SageMaker Pipelines is a purpose-built workflow orchestration service to automate machine learning (ML) development. SageMaker Pipelines is specifically designed to orchestrate end-to-end ML workflows, integrating data processing, model training, hyperparameter tuning, and deployment in a seamless manner. It provides built-in versioning, lineage tracking, and support for continuous integration and delivery (CI/CD), making it the best choice for this use case.

via – https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines.html

Incorrect options:

Use AWS Step Functions to define and orchestrate each step of the ML workflow, integrate with SageMaker for model training and deployment, and leverage AWS Lambda for data preprocessing tasks – AWS Step Functions is a powerful service for orchestrating workflows, and it can integrate with SageMaker and Lambda. However, using Step Functions for the entire ML workflow adds complexity since it requires coordinating multiple services, whereas SageMaker Pipelines provides a more seamless, integrated solution for ML-specific workflows.

Leverage Amazon EC2 instances to manually execute each step of the ML workflow, use Amazon RDS for storing intermediate results, and deploy the model using Amazon SageMaker endpoints – Manually managing each step of the ML workflow using EC2 instances and RDS is labor-intensive, prone to errors, and not scalable. It also lacks the automation and orchestration capabilities needed for a robust ML workflow.

Use AWS Glue for data preprocessing, Amazon SageMaker for model training and tuning, and manually deploy the model to an Amazon EC2 instance for inference – While using AWS Glue for data preprocessing and SageMaker for training is possible, manually deploying the model on EC2 lacks the orchestration and management features provided by SageMaker Pipelines. This approach also misses out on the integrated tracking, automation, and scalability features offered by SageMaker Pipelines.

Use AWS Glue for data preprocessing, Amazon SageMaker for model training and tuning, and manually deploy the model to an Amazon EC2 instance for inference – While using AWS Glue for data preprocessing and SageMaker for training is possible, manually deploying the model on EC2 lacks the orchestration and management features provided by SageMaker Pipelines. This approach also misses out on the integrated tracking, automation, and scalability features offered by SageMaker Pipelines.

Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines.html