Practice Serving Frameworks - 20.2.2 | 20. Deployment and Monitoring of Machine Learning Models | Data Science Advance
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.

Typing
Memory
Math
English Adventures
Knowledge

20.2.2 - Serving Frameworks

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 related to the topic.

Question 1

Easy

What is the main purpose of a serving framework?

💡 Hint: Think about how models interact with users.

Question 2

Easy

Name one framework used for serving TensorFlow models.

💡 Hint: It's a specific framework tailored for a popular ML library.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does a serving framework do in machine learning?

  • Deploys models
  • Trains models
  • Collects data

💡 Hint: Think about the lifecycle of a machine learning model.

Question 2

True or False: TensorFlow Serving is suitable only for PyTorch models.

  • True
  • False

💡 Hint: Recall which ML library each framework is associated with.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a well-trained TensorFlow model that provides real-time predictions for a website traffic application. What steps would you take to deploy this model using TensorFlow Serving in a scalable manner?

💡 Hint: Think about what each tool is responsible for in the deployment process.

Question 2

Discuss the trade-offs between using a lightweight framework like Flask vs a more comprehensive platform like MLflow for serving models. What factors would influence your choice?

💡 Hint: Consider the project's size, complexity, and need for tracking.

Challenge and get performance evaluation