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

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform batch prediction on a dataset using a trained model.

MLOps

predictions = model.[1](X_batch)

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

Atransform

Bfit

Cpredict

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of predict.

Using transform which is for feature transformations.

The predict method is used to generate predictions from the model on a batch of input data.

2fill in blank

medium

Complete the code to serve a real-time prediction request using a deployed model.

MLOps

def serve_request(input_data):
    prediction = model.[1](input_data)
    return prediction

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

Apredict

Bevaluate

Ctrain

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit or train instead of predict.

Using evaluate which is for testing model performance.

For real-time serving, the model uses predict to generate a prediction for the incoming single input.

3fill in blank

hard

Fix the error in the batch prediction code by completing the blank.

MLOps

batch_predictions = model.predict([1])

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

Afit

By_batch

Cmodel

DX_batch

Attempts:

3 left

💡 Hint

Common Mistakes

Passing labels y_batch to predict.

Passing the model itself or calling fit instead.

The input to predict should be the batch features X_batch, not labels or other variables.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps input IDs to their batch predictions.

MLOps

id_to_prediction = {id: [1] for id, [2] in zip(ids, batch_data)}

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

Amodel.predict([batch_data])

Bmodel.predict({{BLANK_2}})

Cdata

Dfeatures

Attempts:

3 left

💡 Hint

Common Mistakes

Using model.predict([batch_data]) which passes a list of the whole batch.

Using a wrong variable name for the batch item.

The dictionary comprehension maps each id to the prediction from the model on the corresponding features.

5fill in blank

hard

Fill all three blanks to complete the real-time serving function that preprocesses input, predicts, and returns the result.

MLOps

def serve(input_raw):
    processed = [1](input_raw)
    prediction = model.[2](processed)
    return [3]

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

Apreprocess

Bpredict

Cprediction

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Returning the processed data instead of the prediction.

Using transform instead of predict for model output.

The function preprocesses the raw input, predicts using the model, and returns the prediction.

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