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.

Practice

1. What is the main difference between batch inference and real-time inference in NLP?

easy

A. Batch inference requires internet connection, real-time inference does not.

B. Batch inference is slower than real-time inference because it uses outdated models.

C. Real-time inference processes data only at night, batch inference runs during the day.

D. Batch inference processes many inputs together, while real-time inference processes inputs one by one quickly.

Solution

1. Step 1: Understand batch inference

   Batch inference means processing many inputs together in one go, which is efficient for large data.

2. Step 2: Understand real-time inference

   Real-time inference means processing each input immediately to give instant results.

3. Final Answer:

   Batch inference processes many inputs together, while real-time inference processes inputs one by one quickly. -> Option D

4. Quick Check:

   Batch = many inputs, Real-time = instant input [OK]

Hint: Batch = many at once, real-time = one fast [OK]

Common Mistakes:

• Confusing batch with outdated models
• Thinking real-time only runs at specific times
• Mixing internet requirements

2. Which code snippet correctly represents a batch inference call for an NLP model?

easy

A. model.load('batch')

B. model.predict('text1')

C. model.predict(['text1', 'text2', 'text3'])

D. model.train(['text1', 'text2'])

Solution

1. Step 1: Identify batch input format

   Batch inference requires passing multiple inputs together, usually as a list or array.

2. Step 2: Check code options

   model.predict(['text1', 'text2', 'text3']) passes a list of texts to predict, which is correct for batch inference.

3. Final Answer:

   model.predict(['text1', 'text2', 'text3']) -> Option C

4. Quick Check:

   Batch input = list of texts [OK]

Hint: Batch inference uses list input for prediction [OK]

Common Mistakes:

• Passing single string instead of list
• Confusing training with inference
• Using unrelated method like load

3. Given the code below, what will be the output type of results?

texts = ['hello', 'world']
results = model.predict(texts)

Assuming model.predict returns predictions for each input.

medium

A. A list of predictions, one for each input text

B. A single prediction combining all texts

C. An error because input is a list

D. A dictionary with input texts as keys

Solution

1. Step 1: Understand input to model.predict

   The input is a list of texts, so the model will process each text separately.

2. Step 2: Understand output type for batch input

   For batch input, the output is usually a list of predictions, matching the input size.

3. Final Answer:

   A list of predictions, one for each input text -> Option A

4. Quick Check:

   Batch input gives list output [OK]

Hint: Batch input returns list output matching inputs [OK]

Common Mistakes:

• Expecting single combined prediction
• Thinking list input causes error
• Assuming output is a dictionary

4. Identify the error in this real-time inference code snippet:

input_text = ['Hello world']
prediction = model.predict(input_text)

Assuming model.predict expects a single string for real-time inference.

medium

A. Input should be a string, not a list

B. model.predict cannot process text

C. Missing batch size parameter

D. Prediction variable name is invalid

Solution

1. Step 1: Check input type for real-time inference

   Real-time inference expects a single input string, not a list.

2. Step 2: Identify mismatch in code

   The code passes a list with one string, causing a type mismatch error.

3. Final Answer:

   Input should be a string, not a list -> Option A

4. Quick Check:

   Real-time input = string only [OK]

Hint: Real-time input must be a single string [OK]

Common Mistakes:

• Passing list instead of string
• Assuming batch size needed for real-time
• Thinking variable name causes error

5. You have a large dataset of 10,000 sentences to classify using an NLP model. You want to minimize total processing time but can wait a few minutes for results. Which inference method should you choose and why?

hard

A. Neither, you should retrain the model first.

B. Batch inference, because processing many inputs together is more efficient for large data.

C. Real-time inference, because it processes each sentence instantly.

D. Real-time inference, because it uses less memory.

Solution

1. Step 1: Analyze dataset size and time constraints

   With 10,000 sentences and willingness to wait minutes, efficiency matters more than instant results.

2. Step 2: Choose inference method based on efficiency

   Batch inference processes many inputs together, reducing overhead and total time.

3. Final Answer:

   Batch inference, because processing many inputs together is more efficient for large data. -> Option B

4. Quick Check:

   Large data + wait time = batch inference [OK]

Hint: Large data with wait time? Use batch inference [OK]

Common Mistakes:

• Choosing real-time for large batch
• Thinking retraining is needed
• Assuming real-time uses less memory always