Batch vs real-time inference in NLP

Introduction

We use batch and real-time inference to get predictions from models. Batch inference handles many inputs at once, while real-time inference gives quick answers one by one.

When you want to analyze a large set of customer reviews all at once.

When you need instant translation of a sentence while chatting.

When processing daily logs overnight to find trends.

When a chatbot must reply immediately to user questions.

When updating recommendations for many users in one go.

Syntax

NLP

Batch inference:
model.predict(batch_of_inputs)

Real-time inference:
model.predict(single_input)

Batch inference processes many inputs together, which is efficient for large data.

Real-time inference processes one input at a time, focusing on speed and low delay.

Examples

This runs predictions on two texts together using batch inference.

NLP

batch_inputs = ["I love this product!", "Not good at all."]
predictions = model.predict(batch_inputs)

This gets a prediction for one input quickly using real-time inference.

NLP

single_input = "How's the weather today?"
prediction = model.predict([single_input])

Sample Model

This example trains a simple text classifier. Then it shows batch inference on two texts and real-time inference on one text.

NLP

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression

# Sample training data
texts = ["I love this movie", "This movie is bad", "Great film", "Terrible film"]
labels = [1, 0, 1, 0]  # 1=positive, 0=negative

# Create vectorizer and model
vectorizer = CountVectorizer()
X_train = vectorizer.fit_transform(texts)
model = LogisticRegression()
model.fit(X_train, labels)

# Batch inference
batch_texts = ["I love this", "Bad movie"]
X_batch = vectorizer.transform(batch_texts)
batch_preds = model.predict(X_batch)

# Real-time inference
single_text = "Great movie"
X_single = vectorizer.transform([single_text])
single_pred = model.predict(X_single)

print("Batch predictions:", batch_preds)
print("Real-time prediction:", single_pred)

Important Notes

Batch inference is usually faster per input but has some delay before results.

Real-time inference is slower per input but gives immediate results.

Choosing depends on whether you need speed or processing many inputs at once.

Summary

Batch inference processes many inputs together for efficiency.

Real-time inference processes one input quickly for instant results.

Use batch for large data and real-time for immediate responses.