Batch prediction vs real-time serving in MLOps - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Key difference between batch prediction and real-time serving

Which statement best describes the main difference between batch prediction and real-time serving in machine learning?

A Batch prediction processes many data points at once periodically, while real-time serving predicts instantly for individual requests.

B Real-time serving only works with small datasets, batch prediction only works with large datasets.

C Batch prediction is always more accurate than real-time serving because it uses more data.

D Batch prediction requires manual input for each prediction, real-time serving automates all predictions.

💡 Hint

Think about how often predictions are made and how many data points are handled at once.

Batch prediction runs on large groups of data at scheduled times, while real-time serving responds immediately to single data inputs.

❓ Model Choice

intermediate

Choosing prediction method for fraud detection

You are building a fraud detection system that must flag suspicious transactions immediately as they happen. Which prediction method is best?

A Batch prediction, running once a day on all transactions.

B Real-time serving, predicting only after collecting 100 transactions.

C Batch prediction, running once a week on all transactions.

D Real-time serving, predicting on each transaction as it occurs.

💡 Hint

Consider how fast the system needs to respond to new data.

Real-time serving is needed to flag fraud instantly, batch prediction delays detection.

❓ Metrics

advanced

Evaluating latency in batch vs real-time prediction

You measure the average latency (time to get prediction) for batch prediction and real-time serving. Which is true?

A Batch prediction has higher latency per data point than real-time serving.

B Both have the same latency per data point.

C Real-time serving has higher latency per data point than batch prediction.

D Latency cannot be measured for batch prediction.

💡 Hint

Think about how predictions are processed individually or in groups.

Real-time serving predicts instantly but each prediction takes some time; batch prediction processes many points together, lowering average latency per point.

🔧 Debug

advanced

Identifying issue in real-time serving system

A real-time serving system is suddenly slow and sometimes fails to respond. Which is the most likely cause?

A The model used is too simple and cannot handle the data volume.

B The real-time serving system is overloaded with too many simultaneous requests.

C The batch prediction job is running too frequently and blocking resources.

D The data used for batch prediction is outdated.

💡 Hint

Consider what happens when many users request predictions at once.

Real-time serving can slow or fail if too many requests overload the system, causing delays or errors.

❓ Predict Output

expert

Output of batch vs real-time prediction code snippet

Given the code below, what is the output?

MLOps

def batch_predict(model, data):
    return [model(x) for x in data]

def real_time_predict(model, x):
    return model(x)

model = lambda x: x * 2
batch_data = [1, 2, 3]

batch_result = batch_predict(model, batch_data)
real_time_result = real_time_predict(model, 4)

print(batch_result, real_time_result)

A [2, 4, 6] 8

B [1, 2, 3] 4

C [2, 4, 6] [8]

D Error: model is not callable

💡 Hint

Look at how the model function is applied to data in batch and real-time.

The model doubles each input. Batch prediction applies it to a list, real-time applies it to one value.

Practice

1. What is the main difference between batch prediction and real-time serving in machine learning?

easy

A. Batch prediction is faster than real-time serving for single inputs.

B. Real-time serving is used only for training models.

C. Batch prediction processes many inputs at once, while real-time serving processes one input at a time.

D. Batch prediction requires internet connection, real-time serving does not.

Solution

1. Step 1: Understand batch prediction

   Batch prediction processes a large number of inputs together, usually offline or in scheduled jobs.

2. Step 2: Understand real-time serving

   Real-time serving handles one input at a time to provide instant predictions.

3. Final Answer:

   Batch prediction processes many inputs at once, while real-time serving processes one input at a time. -> Option C

4. Quick Check:

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

Hint: Batch = many inputs; real-time = one input fast [OK]

Common Mistakes:

• Confusing batch with real-time speed
• Thinking real-time is for training
• Assuming batch needs internet

2. Which of the following is the correct way to describe real-time serving in a sentence?

easy

A. Real-time serving provides predictions instantly for each individual input.

B. Real-time serving delays predictions until batch processing is complete.

C. Real-time serving is only used for model training.

D. Real-time serving processes data in large groups at scheduled times.

Solution

1. Step 1: Identify real-time serving purpose

   Real-time serving is designed to give instant predictions for each input as it arrives.

2. Step 2: Eliminate incorrect options

   Options A, B, and C describe batch or training, not real-time serving.

3. Final Answer:

   Real-time serving provides predictions instantly for each individual input. -> Option A

4. Quick Check:

   Instant prediction per input = real-time serving [OK]

Hint: Real-time = instant single input prediction [OK]

Common Mistakes:

• Mixing batch processing with real-time
• Thinking real-time is for training
• Confusing delay with instant response

3. Consider this Python pseudocode for batch prediction and real-time serving:

def batch_predict(data_list):
    return [model.predict(x) for x in data_list]

def real_time_predict(single_input):
    return model.predict(single_input)

batch_result = batch_predict([1, 2, 3])
real_time_result = real_time_predict(4)
print(batch_result, real_time_result)

What will be printed?

medium

A. pred1 pred2 pred3 pred4

B. [pred1, pred2, pred3] pred4

C. [pred1, pred2, pred3, pred4] None

D. Error because batch_predict expects a single input

Solution

1. Step 1: Understand batch_predict output

   batch_predict returns a list of predictions for each input in data_list, so batch_result is a list [pred1, pred2, pred3].

2. Step 2: Understand real_time_predict output

   real_time_predict returns a single prediction for the single input 4, so real_time_result is pred4.

3. Final Answer:

   [pred1, pred2, pred3] pred4 -> Option B

4. Quick Check:

   Batch returns list, real-time returns single prediction [OK]

Hint: Batch returns list; real-time returns single value [OK]

Common Mistakes:

• Thinking batch returns single prediction
• Confusing print output format
• Assuming error due to input type

4. You have this code snippet for real-time serving:

def real_time_predict(input):
    predictions = []
    for x in input:
        predictions.append(model.predict(x))
    return predictions

result = real_time_predict(5)
print(result)

What is the error and how to fix it?

medium

A. Error: input is not iterable; fix by passing a list like [5].

B. Error: model.predict is undefined; fix by importing model.

C. No error; code runs correctly.

D. Error: predictions list is not returned; fix by adding return statement.

Solution

1. Step 1: Identify input type issue

   The function expects input to be iterable (like a list), but 5 is an integer and not iterable.

2. Step 2: Fix by passing iterable

   Passing [5] (a list with one element) makes the loop work correctly.

3. Final Answer:

   Error: input is not iterable; fix by passing a list like [5]. -> Option A

4. Quick Check:

   Non-iterable input causes error [OK]

Hint: Check if input is iterable for loops [OK]

Common Mistakes:

• Passing single value instead of list
• Ignoring error message about iteration
• Assuming model.predict missing

5. A company wants to predict customer churn. They have 1 million customers and want to update predictions once a day. They also want to offer instant offers to customers calling support. Which approach fits best?

hard

A. Use batch prediction for support calls and real-time serving for daily updates.

B. Use only real-time serving for all predictions to keep data fresh.

C. Use batch prediction only and ignore real-time serving.

D. Use batch prediction once a day for all customers, and real-time serving for support calls.

Solution

1. Step 1: Analyze batch prediction use case

   Predicting churn for 1 million customers once a day fits batch prediction well because it handles large data offline.

2. Step 2: Analyze real-time serving use case

   Instant offers during support calls require quick predictions, so real-time serving is best.

3. Final Answer:

   Use batch prediction once a day for all customers, and real-time serving for support calls. -> Option D

4. Quick Check:

   Batch for bulk daily, real-time for instant [OK]

Hint: Batch for bulk jobs; real-time for instant needs [OK]

Common Mistakes:

• Using real-time for all large data
• Ignoring instant prediction needs
• Mixing batch and real-time roles