Model serving for NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is model serving in NLP?

Model serving in NLP means making a trained language model available so it can answer questions or analyze text in real time, like a helpful assistant ready to respond anytime.

beginner

Why do we need model serving for NLP applications?

We need model serving so users or other programs can easily get answers or insights from the NLP model without retraining it every time. It helps deliver fast and consistent results.

intermediate

Name two common ways to serve NLP models.

Two common ways are: 1) Using a REST API where the model listens for text requests and sends back answers, and 2) Using batch processing where many texts are analyzed at once and results saved.

intermediate

What is latency in model serving and why does it matter?

Latency is the time it takes for the model to respond after receiving a request. Lower latency means faster answers, which is important for good user experience in chatbots or translators.

advanced

How can you improve the scalability of NLP model serving?

You can improve scalability by using multiple servers to share the work, caching frequent answers, or simplifying the model to respond faster when many users ask at once.

What does model serving allow you to do with an NLP model?

A Use the model to answer new text inputs in real time

B Train the model faster

C Delete the model from memory

D Convert the model to images

Model serving lets you use the trained NLP model to respond to new text inputs quickly.

Which of these is a common way to serve an NLP model?

A REST API

B Image rendering

C Manual coding

D Spreadsheet formulas

REST API is a common method to serve NLP models by handling requests and responses over the web.

Why is low latency important in NLP model serving?

A It trains the model better

B It increases the model size

C It makes the model respond faster to users

D It slows down the server

Low latency means the model answers quickly, improving user experience.

What does scalability mean in the context of model serving?

A Ability to delete old data

B Ability to change the model's language

C Ability to reduce model accuracy

D Ability to handle many requests at once

Scalability means the system can serve many users or requests without slowing down.

Which technique can help improve scalability of NLP model serving?

A Ignoring user requests

B Using multiple servers

C Deleting the model

D Increasing model complexity

Using multiple servers spreads the work and helps serve more users efficiently.

Explain what model serving is and why it is important for NLP applications.

Think about how you get answers from a language app anytime.

Describe two common methods to serve NLP models and one challenge related to serving.

Consider how models handle single requests vs many at once.