Batch vs real-time inference in NLP - Model Approaches Compared

Model Pipeline - Batch vs real-time inference

This pipeline shows how a natural language processing model makes predictions in two ways: batch inference processes many texts at once, while real-time inference processes one text immediately.

Data Flow - 6 Stages

1Input Text Data

10000 texts x variable length→Collect raw text data for processing→10000 texts x variable length

"I love sunny days", "The movie was great", "How is the weather?"

↓

2Text Preprocessing

10000 texts x variable length→Clean and tokenize texts (lowercase, remove punctuation, split words)→10000 texts x 20 tokens (max)

["i", "love", "sunny", "days"]

↓

3Feature Extraction

10000 texts x 20 tokens→Convert tokens to numeric vectors using word embeddings→10000 texts x 20 tokens x 50 features

[[0.12, -0.05, ...], [0.33, 0.01, ...], ...]

↓

4Model Inference (Batch)

10000 texts x 20 tokens x 50 features→Run model on all texts at once to predict sentiment→10000 texts x 3 classes

[[0.1, 0.8, 0.1], [0.7, 0.2, 0.1], ...]

↓

5Model Inference (Real-time)

1 text x 20 tokens x 50 features→Run model on single text immediately to predict sentiment→1 text x 3 classes

[0.2, 0.7, 0.1]

↓

6Output Predictions

variable (batch or single) x 3 classes→Select class with highest probability as prediction→variable (batch or single) x 1 label

["Positive", "Negative", "Neutral"]

Training Trace - Epoch by Epoch

Loss
1.2 |*       
1.0 | *      
0.8 |  *     
0.6 |   *    
0.4 |    *   
0.2 |     *  
0.0 +--------
      1 3 5 7 10 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning, loss high, accuracy low
3	0.8	0.65	Loss decreases, accuracy improves
5	0.5	0.78	Model converging, better predictions
7	0.35	0.85	Loss low, accuracy high, training stable
10	0.3	0.88	Final epoch, model well trained

Prediction Trace - 5 Layers

Layer 1: Input Text

Layer 2: Text Preprocessing

Layer 3: Feature Extraction

Layer 4: Model Inference

Layer 5: Prediction Output

Model Quiz - 3 Questions

What is the main difference between batch and real-time inference?

A Batch processes one text immediately; real-time processes many texts at once

B Batch processes many texts at once; real-time processes one text immediately

C Batch inference is slower than training; real-time is faster than training

D Batch inference uses different models than real-time inference

Key Insight

Batch inference is efficient for processing many texts together, while real-time inference is designed for quick responses to single inputs. Training shows steady improvement in loss and accuracy, confirming the model learns patterns well. Prediction outputs probabilities that help decide the final class label.