Sky Cone

Week 1: Overview of the ML Lifecycle and Deployment

Section 2: Graded Assessment

1. The Machine Learning Project Lifecycle

Question 1
Which of these are stages of the machine learning project lifecycle? Check all that apply.

1. Modeling
2. Deployment
3. Scoping
4. Data
5. Configuration

Question 2
Which of these is not an advantage of a typical edge deployment compared to a typical cloud deployment?

1. More computational power available
2. Less network bandwidth needed
3. Lower latency
4. Can function even if network connection is down

Question 3
In the speech recognition example, what is the problem with some labelers transcribing audio as "Um, today's weather" and others transcribing "Umm... today's weather"?

1. The second is grammatically incorrect and we should use the first transcription.
2. Either transcription is okay, but the inconsistency is problematic.
3. The first is grammatically incorrect and we should use the second transcription.
4. We should not be transcribing "Umm." The correct transcription, which serves the user's needs better, is just "Today's weather".

Question 4
After a system is deployed, monitoring and maintaining the system will help us handle cases of concept drift or data drift.

1. False
2. True

Question 5
Which statement is a more accurate description of the full cycle of a machine learning project?

1. It is a linear process, in which we move step-by-step from scoping to deployment. (That's why we call it a cycle. Bicycles are only good at going forward, not backward.)
2. It is an iterative process, where during a later stage we might go back to an earlier stage. (That’s why we call it a cycle--it's a circular process.)