Batch prediction vs real-time serving in MLOps - Quick Revision & Key Differences

Recall & Review

beginner

What is batch prediction in machine learning?

Batch prediction means making predictions on a large group of data all at once, usually at scheduled times. It's like baking a whole batch of cookies instead of one at a time.

beginner

What does real-time serving mean in ML?

Real-time serving means making predictions instantly as new data comes in, like answering a question right away instead of waiting.

intermediate

Name one advantage of batch prediction.

Batch prediction is efficient for large amounts of data and can be scheduled during low-traffic times to save resources.

intermediate

Why might real-time serving be more costly than batch prediction?

Real-time serving requires always-on systems to respond instantly, which uses more computing power and resources continuously.

beginner

Give an example use case for batch prediction and one for real-time serving.

Batch prediction: monthly credit risk scoring for many customers. Real-time serving: fraud detection during a credit card transaction.

Which scenario best fits batch prediction?

A Scoring thousands of customer profiles once a day

B Responding instantly to chatbot questions

C Predicting weather every hour as new data arrives

D Detecting fraud during a live transaction

Batch prediction processes many data points at once, like scoring customers daily.

What is a key feature of real-time serving?

A Processes data in large groups

B Provides instant predictions as data arrives

C Runs predictions on a schedule

D Requires no computing resources

Real-time serving gives immediate predictions when new data comes in.

Why might batch prediction be preferred over real-time serving?

A It requires no data preprocessing

B It is always faster

C It provides instant results

D It uses less computing power for large data sets

Batch prediction is efficient for large data and can save resources by running at scheduled times.

Which is a disadvantage of real-time serving?

A Needs continuous resource availability

B Does not support instant predictions

C Only works with historical data

D Cannot handle large data volumes

Real-time serving requires systems to be always ready, which can be costly.

Which use case is best for real-time serving?

A Monthly sales forecasting

B Batch image classification overnight

C Live fraud detection during payment

D Annual customer segmentation

Live fraud detection needs instant predictions, so real-time serving fits best.

Explain the main differences between batch prediction and real-time serving in machine learning.

Think about how and when predictions are made and the resource needs.

Describe a situation where batch prediction is better than real-time serving and why.

Consider tasks that can wait and happen in bulk.