AML AML Model Deployment & MLOps

Question 1

What is MLOps in the context of advanced machine learning?

Accepted Answer

The practice of combining ML development with operations to deploy and maintain models in production

Answer

MLOps applies DevOps principles to machine learning, automating training, deployment, monitoring, and retraining workflows.

Question 2

What is model drift?

Accepted Answer

The degradation of model performance as real-world data distribution changes from training data

Answer

Model drift occurs when the statistical properties of input data or target relationships change post-deployment, reducing model accuracy.

Question 3

Which tool is commonly used to containerize ML models for consistent deployment across environments?

Accepted Answer

Docker

Answer

Docker packages ML models with their dependencies into portable containers that run consistently across development and production environments.

Question 4

What is a REST API in the context of machine learning model deployment?

Accepted Answer

A standardized interface that allows applications to send data to a model and receive predictions via HTTP

Answer

A REST API exposes model inference as HTTP endpoints so any downstream application can request predictions via standard web requests.

Question 5

What is A/B testing in the context of ML model deployment?

Accepted Answer

Comparing two model versions by routing live traffic to each and measuring performance differences

Answer

A/B testing routes a portion of real production traffic to a challenger model while the rest serves the incumbent, enabling data-driven model selection.

(AML) Advanced Machine Learning Certification Practice Test

(AML) Advanced Machine Learning Certification Practice Test

AML AML Model Deployment & MLOps