Practice Deployment Scenarios - 20.1.2 | 20. Deployment and Monitoring of Machine Learning Models | Data Science Advance
Students

Academic Programs

AI-powered learning for grades 8-12, aligned with major curricula

Professional

Professional Courses

Industry-relevant training in Business, Technology, and Design

Games

Interactive Games

Fun games to boost memory, math, typing, and English skills

Deployment Scenarios

20.1.2 - Deployment Scenarios

Enroll to start learning

You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is batch inference?

💡 Hint: Think about periodic processing of data.

Question 2 Easy

Name a scenario where online inference is utilized.

💡 Hint: Consider instant feedback applications.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does batch inference involve?

Real-time predictions
Predictions on large datasets at intervals
Only predictions on small datasets

💡 Hint: Think about how often predictions are made.

Question 2

Online inference is best for scenarios where?

True
False

💡 Hint: Consider applications that benefit from instant feedback.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Create a strategy for an online retail platform planning to transition from batch inference to online inference. Discuss potential challenges and solutions.

💡 Hint: Focus on balancing efficiency and user experience.

Challenge 2 Hard

How would the deployment of an ML model for a self-driving car involve aspects of both edge and online inference? Discuss operational advantages.

💡 Hint: Consider the implications of decision-making speed for safety and efficiency.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.