[AiLab] Generic ML predict block v1.0 #38428
Closed
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Erin007
commented
Jan 6, 2021
apps/src/lib/util/mlApi.js
Outdated
|
|
||
| export default function MLApi() {} | ||
|
|
||
| MLApi.getModelNames = function() { |
There was a problem hiding this comment.
This is where things fall apart because the names aren't back from the call in time to show in the block dropdown -- dead end or am I missing a better way to do something similar?
There was a problem hiding this comment.
Erin007
changed the title
This was referenced Jan 8, 2021
|
This change got updated and rolled into #38664 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Hidden behind the
?enableExperiments=applab-mlexperiment param this is the first iteration of a genericgetPredictionblock. ThegetPredictionblock accepts a model name, testValues object and callback function as parameters.Further revisions will be based on the outcome of discussions based on this doc. In the meantime, the next step is to add in the code that will fetch the trained model, call to the ML libraries and return a prediction in
mlApi.js.