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An AI system recommends New Year's resolutions. It has an ML pipeline without monitoring components. What retraining strategy would be BEST for this pipeline?
Suggested Answer: B
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A support-vector machine (SVM) is a supervised learning algorithm that can be used for classification or regression problems. An SVM tries to find an optimal hyperplane that separates the data into different categories or classes. However, sometimes the data is not linearly separable, meaning there is no straight line or plane that can separate them. In such cases, a polynomial kernel can help improve the prediction of the SVM by transforming the data into a higher-dimensional space where it becomes linearly separable. A polynomial kernel is a function that computes the similarity between two data points using a polynomial function of their features.
36 comments · newest first
MA
Marsha
11 months ago
D could be important too, especially with changing trends!
JI
Jill
11 months ago
A is a good idea, but it might not be enough.
PA
Paola
11 months ago
Wait, can you really detect concept drift? Sounds complicated!
KE
Keena
12 months ago
I disagree, B seems more consistent for yearly resolutions.
OD
Odette
12 months ago
I think option C makes the most sense for retraining.
TA
Tamra
1 year ago
I lean towards option B since it suggests a regular schedule, but I wonder if it’s enough without monitoring for changes.
TH
Theresia
1 year ago
I practiced a similar question where timing was key. I feel like option A might be too rigid for a dynamic system like this.
XO
Xochitl
1 year ago
I'm not sure, but I remember something about data drift being important too. Maybe option D could be relevant?
HO
Horace
1 year ago
I think option C about concept drift makes sense because people's resolutions can change over time, right?
GL
Glendora
1 year ago
Option B, retraining periodically every year, seems like a good baseline approach. That way we can stay on top of changes without relying on potentially unreliable drift detection.
HE
Heike
1 year ago
I'm leaning towards option C - retraining when concept drift is detected. Since the system is recommending New Year's resolutions, the underlying data and patterns could shift significantly over time, so we'd want to be responsive to that.
MA
Marcos
1 year ago
Hmm, I'm a bit confused. Does the lack of monitoring components mean we can't detect concept or data drift? That could impact the best retraining strategy.
PH
Phuong
1 year ago
This seems like a tricky one. I'll need to think carefully about the different retraining strategies and how they might apply to this specific scenario.
VI
Virgie
1 year ago
Okay, I think I've got this. Since R-LFA and TI-LFA are both enabled, R1 will use an R-LFA path that doesn't coincide with the post-convergence path. Option B looks like the right answer.
YA
Yan
1 year ago
I think the REST API is the way to go here. It's the most modern and flexible option for accessing the Attribute Group schema.
JE
Jennifer
1 year ago
I've got this! The IP address and netmask of the host via the management port are the two parameters that can be set during the initial BIG-IP system configuration. Easy peasy.
TR
Trinidad
1 year ago
I'm just hoping the AI doesn't start suggesting resolutions like 'become a professional couch potato' or 'learn to speak fluent gibberish'.
TI
Tiara
1 year ago
B) Periodically every year
IR
Irving
1 year ago
C) When concept drift is detected
EL
Eleonora
1 year ago
A) Periodically before New Year's Day and after New Year's Day
QU
Quentin
2 years ago
Haha, imagine an AI system recommending resolutions like 'eat more ice cream' or 'nap for 12 hours a day'. Definitely need to keep a close eye on that concept drift!
SH
Sharen
1 year ago
Kayleigh: Agreed, we need to make sure it's giving out helpful recommendations.
KA
Kayleigh
1 year ago
User 2: Definitely! We should retrain the AI system when concept drift is detected.
HE
Hershel
1 year ago
User 1: Haha, that would be funny! 'Eat more ice cream' as a resolution.
CA
Carey
2 years ago
D) When data drift is detected could also work, but concept drift is more important for this use case. The system needs to understand the changing meanings of 'New Year's resolutions' over time.
TA
Tammi
2 years ago
I agree, C is the way to go. Retraining based on concept drift will ensure the system stays aligned with the user's evolving needs.
PE
Pete
1 year ago
I agree, C is the way to go. Retraining based on concept drift will ensure the system stays aligned with the user's evolving needs.
BE
Benton
1 year ago
C) When concept drift is detected
GE
Georgeanna
1 year ago
B) Periodically every year
RO
Roosevelt
1 year ago
A) Periodically before New Year's Day and after New Year's Day
CA
Carmen
2 years ago
C) When concept drift is detected seems like the best option. Monitoring for concept drift is crucial for an AI system that makes recommendations, as the user's preferences and the relevance of the resolutions can change over time.
LY
Lyda
1 year ago
User 2: I agree, the AI system needs to adapt to changes in user preferences.
LY
Lyndia
1 year ago
User 1: I think retraining when concept drift is detected is important for accurate recommendations.
MI
Mitzie
2 years ago
But wouldn't it also be helpful to retrain periodically every year to ensure the recommendations stay relevant?
CA
Candra
2 years ago
I agree with Josephine. It's important to update the AI system's recommendations when there are changes in the data distribution.
JO
Josephine
2 years ago
I think the best retraining strategy would be when concept drift is detected.