Batch vs real-time inference in NLP - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform batch inference on a list of texts using a model.

NLP

predictions = model.predict([1])

Drag options to blanks, or click blank then click option'

Asentence

Btext

Ctexts

Dinput_text

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single string instead of a list causes errors or wrong predictions.

The model expects a list of texts for batch inference, so we pass texts.

2fill in blank

medium

Complete the code to perform real-time inference on a single input text.

NLP

result = model.predict([1])

Drag options to blanks, or click blank then click option'

Atexts

Btext

Cbatch

Dinputs

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list instead of a single string causes shape errors.

Real-time inference processes one input at a time, so we pass text.

3fill in blank

hard

Fix the error in the batch inference code by choosing the correct input format.

NLP

outputs = model.predict([1])

Drag options to blanks, or click blank then click option'

Atexts

B[text]

Ctext

Dtext.split()

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single string or a list with one string inside causes unexpected results.

Batch inference requires a list of texts, so texts is correct.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each text to its prediction in batch mode.

NLP

results = {text: model.predict([1]) for text in [2]

Drag options to blanks, or click blank then click option'

A[text]

Btext

Ctexts

Dtext.split()

Attempts:

3 left

💡 Hint

Common Mistakes

Passing text directly without wrapping causes errors.

Iterating over a single string instead of the list of texts.

For each text in texts, we pass a list containing that single text [text] to model.predict because the model expects a list input.

5fill in blank

hard

Fill all three blanks to create a batch inference dictionary that filters texts longer than 5 words and maps them to predictions.

NLP

filtered_results = { [1]: model.predict([[2]]) for [3] in texts if len([1].split()) > 5 }

Drag options to blanks, or click blank then click option'

Atext

Dsentence

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Not wrapping the input in a list for prediction.

We use text as the variable name consistently. The comprehension iterates over texts, filters texts longer than 5 words, and predicts on each wrapped in a list.

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