Batch prediction vs real-time serving in MLOps - Hands-On Comparison

Batch Prediction vs Real-Time Serving in MLOps

📖 Scenario: You work as a machine learning engineer in a company that predicts customer churn. You want to compare two ways to get predictions from your model: batch prediction and real-time serving.Batch prediction means you run the model on many customers at once, like a nightly job. Real-time serving means you get predictions instantly when a customer interacts with your app.

🎯 Goal: Build a simple Python program that simulates batch prediction and real-time serving using a dummy model. You will create data, configure a threshold, apply prediction logic, and print the results.

📋 What You'll Learn

Create a list of customer IDs and their feature values

Set a prediction threshold variable

Write a function to simulate model prediction

Use batch prediction to predict for all customers

Use real-time serving to predict for one customer

Print both batch and real-time prediction results

💡 Why This Matters

🌍 Real World

Companies use batch prediction to process large amounts of data overnight, saving resources. Real-time serving is used when instant decisions are needed, like fraud detection or personalized recommendations.

💼 Career

Understanding batch vs real-time prediction is key for MLOps engineers to design efficient and responsive machine learning systems.

1

Create customer data list

Create a list called customers with these exact entries: (101, 0.2), (102, 0.8), (103, 0.5), (104, 0.9). Each tuple has a customer ID and a feature value.

MLOps

# Create the list called customers with the given tuples
# Your code here

Hint

Use a list of tuples with the exact customer IDs and feature values.

2

Set prediction threshold

Create a variable called threshold and set it to 0.6.

MLOps

customers = [(101, 0.2), (102, 0.8), (103, 0.5), (104, 0.9)]
# Set the threshold variable to 0.6
# Your code here

Hint

Just assign 0.6 to the variable named threshold.

3

Write prediction function and batch prediction

Define a function called predict that takes a feature value and returns True if the feature is greater than threshold, otherwise False. Then create a list called batch_results using a list comprehension that applies predict to each customer's feature in customers.

MLOps

customers = [(101, 0.2), (102, 0.8), (103, 0.5), (104, 0.9)]
threshold = 0.6
# Define predict(feature) function
# Create batch_results list with predictions for all customers
# Your code here

Hint

Use a function with a simple if check and a list comprehension to apply it to all customers.

4

Print batch and real-time prediction results

Print the batch_results list. Then print the prediction result for customer ID 105 with feature value 0.7 using the predict function (simulate real-time serving).

MLOps

customers = [(101, 0.2), (102, 0.8), (103, 0.5), (104, 0.9)]
threshold = 0.6

def predict(feature):
    return feature > threshold

batch_results = [predict(feature) for _, feature in customers]
# Print batch_results
# Print prediction for customer 105 with feature 0.7
# Your code here

Hint

Use print statements to show the batch list and the single prediction result.

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