AML AML Model Deployment & MLOps 1 — Questions and Answers
Question 1: What is MLOps in the context of advanced machine learning?
- A programming language for machine learning
- The practice of combining ML development with operations to deploy and maintain models in production (Correct answer)
- A type of neural network architecture
- A dataset versioning tool
Correct answer: The practice of combining ML development with operations to deploy and maintain models in production
MLOps applies DevOps principles to machine learning, automating training, deployment, monitoring, and retraining workflows.
Question 2: What is model drift?
- The gradual improvement of a model over time
- The degradation of model performance as real-world data distribution changes from training data (Correct answer)
- The movement of model weights during training
- A regularization technique for production models
Correct answer: The degradation of model performance as real-world data distribution changes from training data
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?
- Jupyter Notebook
- Docker (Correct answer)
- NumPy
- Scikit-learn
Correct answer: Docker
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?
- A database for storing model artifacts
- A standardized interface that allows applications to send data to a model and receive predictions via HTTP (Correct answer)
- A monitoring dashboard for deployed ML models
- A version control system for datasets
Correct answer: A standardized interface that allows applications to send data to a model and receive predictions via HTTP
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?
- Testing models on two separate servers simultaneously
- Comparing two model versions by routing live traffic to each and measuring performance differences (Correct answer)
- Running automated unit tests on model training code
- Testing two different datasets against the same model
Correct answer: Comparing two model versions by routing live traffic to each and measuring performance differences
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.
Question 6: What does CI/CD stand for in an MLOps pipeline?
- Continuous Integration / Continuous Deployment (Correct answer)
- Containerized Inference / Continuous Development
- Central Intelligence / Cloud Deployment
- Continuous Improvement / Continuous Diagnostics
Correct answer: Continuous Integration / Continuous Deployment
CI/CD automates testing and deployment pipelines so new model versions can be validated and released quickly and reliably.
AML AML Model Deployment & MLOps 1
What is MLOps in the context of advanced machine learning?