Batch vs real-time inference in NLP - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding batch inference

Which of the following best describes batch inference in machine learning?

A Training the model using multiple data points in one go.

B Generating predictions instantly for each data point as it arrives.

C Processing many data points together at once, usually offline, to generate predictions.

D Updating the model weights continuously during prediction.

💡 Hint

Think about when predictions are made: all at once or one by one?

Batch inference means processing many inputs together, often offline, to produce predictions in bulk. Real-time inference is instant per input.

🧠 Conceptual

intermediate

Real-time inference use case

Which scenario is best suited for real-time inference?

A Recommending a product immediately after a user clicks on a website.

B Archiving old prediction results for future analysis.

C Training a language model on millions of sentences.

D Analyzing a large dataset overnight to update customer segments.

💡 Hint

Consider when the prediction is needed: instantly or later?

Real-time inference is used when predictions must be made instantly, such as recommending products right after user actions.

❓ Metrics

advanced

Latency comparison between batch and real-time inference

Given a model that takes 0.01 seconds to predict one input, what is the expected latency for batch inference processing 1000 inputs at once compared to real-time inference processing one input at a time?

A Batch latency ~10 seconds; Real-time latency ~0.01 seconds per input.

B Batch latency ~100 seconds; Real-time latency ~1 second per input.

C Batch latency ~0.1 seconds; Real-time latency ~0.1 seconds per input.

D Batch latency ~0.01 seconds; Real-time latency ~10 seconds per input.

💡 Hint

Multiply prediction time by number of inputs for batch; real-time is per input.

Batch inference processes all inputs together, so total time is 0.01s * 1000 = 10s. Real-time inference processes one input at a time, so latency per input is 0.01s.

🔧 Debug

advanced

Identifying error in real-time inference code snippet

What error will this Python code raise when performing real-time inference?

def predict_real_time(model, inputs):
    results = []
    for input in inputs:
        prediction = model.predict(input)
        results.append(prediction)
    return results

# inputs is a list of data points

A NameError because 'input' is a reserved keyword.

B SyntaxError due to incorrect indentation of results.append(prediction).

C TypeError because model.predict expects a batch, not single input.

D The results list will contain only the last prediction, missing others.

💡 Hint

Check where the prediction is added to results inside the loop.

The append is outside the loop, so only the last prediction is added. The list misses all previous predictions.

❓ Model Choice

expert

Choosing model architecture for batch vs real-time inference

You have two model architectures: Model A is large and accurate but slow; Model B is smaller and faster but less accurate. For a chatbot requiring instant replies, which model is best?

A Model A, because accuracy is most important regardless of speed.

B Model B, because real-time inference needs fast responses even if less accurate.

C Use Model A for batch inference and Model B for real-time inference simultaneously.

D Neither, because chatbots should only use rule-based systems.

💡 Hint

Consider the trade-off between speed and accuracy for instant replies.

Real-time inference requires fast predictions to keep conversations smooth, so a smaller, faster model is preferred despite some accuracy loss.

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