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The objective is to build a feature extraction functionality to enhance the feature development while creating a pipeline.
We propose to add a feature extractor into WebUI Pipeline Builder that gives
Motivation
Building a feature extractor for the WebUI unlocks powerful benefits for model development:
Simplified experimentation: Users can easily extract and analyze specific features from data directly within the WebUI, without running a whole pipeline.
Improved model training: Feature extraction allows users to focus models on relevant data aspects, potentially leading to faster training times and better model performance.
Enhanced user experience: Integrating feature extraction into the WebUI empowers users with a much more flexible development, fostering a smoother workflow.
Design Proposal
UI
We propose to build a feature extractor based on the pipeline builder. Also, we propose to use 3 different pipeline builder modes: Feature Extractor, Train Model, AutoML
And change the left nav menu to group these screens
They all have the same home screen with a pipeline table and templates.
Existing pipelines can be opened with any of those modes, which gives the flexibility to build models based on a properly selected set of transforms.
Feature Extractor detailed screen:
To run it calls action submitOptimizationRequest with post request project/${projectUuid}/sandbox-async/${pipelineUuid}/ with parameter
execution_type = pipeline
To get data for the Feature Visualisation and Feature Graph it calls an action with get request project/${projectUuid}/sandbox-async/${pipelineUuid}/
Train Model and AutoML use pipeline builder with different custom_training parameter and execution_type = automl
calls action submitOptimizationRequest with post request project/${projectUuid}/sandbox-async/${pipelineUuid}/
Back-end
The endpoint project/${projectUuid}/sandbox-async/${pipelineUuid}/ should also return
feature_summary
To implement this we need to implement feature_summary calculation base on feature_table similarly to model's feature_summary
The endpoint project/${projectUuid}/sandbox-async/${pipelineUuid}/ should also return result_type field
Performance Implications
There are some extra calculation of feature_summary during get request project/${projectUuid}/sandbox-async/${pipelineUuid}/ for pipelines with execution_type = pipeline
Dependencies
No 3rd party dependencies would be added
User Impact
Benefit: This feature simplifies experimentation, improves model training, and enhances user experience by enabling feature extraction directly within the UI.
Faster workflows: Extract and analyze features without running entire pipelines.
Optimized models: Focus models on relevant data aspects for potentially faster training and better performance.
Smoother development: Integrates seamlessly into UI fostering a streamlined workflow.
The text was updated successfully, but these errors were encountered:
Please identify RFC type
Feature
Description
The objective is to build a feature extraction functionality to enhance the feature development while creating a pipeline.
We propose to add a feature extractor into WebUI Pipeline Builder that gives
Motivation
Building a feature extractor for the WebUI unlocks powerful benefits for model development:
Simplified experimentation: Users can easily extract and analyze specific features from data directly within the WebUI, without running a whole pipeline.
Improved model training: Feature extraction allows users to focus models on relevant data aspects, potentially leading to faster training times and better model performance.
Enhanced user experience: Integrating feature extraction into the WebUI empowers users with a much more flexible development, fostering a smoother workflow.
Design Proposal
UI
We propose to build a feature extractor based on the pipeline builder. Also, we propose to use 3 different pipeline builder modes: Feature Extractor, Train Model, AutoML
And change the left nav menu to group these screens
They all have the same home screen with a pipeline table and templates.
Existing pipelines can be opened with any of those modes, which gives the flexibility to build models based on a properly selected set of transforms.
Feature Extractor detailed screen:
To run it calls action submitOptimizationRequest with post request
project/${projectUuid}/sandbox-async/${pipelineUuid}/
with parameterexecution_type = pipeline
To get data for the Feature Visualisation and Feature Graph it calls an action with get request
project/${projectUuid}/sandbox-async/${pipelineUuid}/
Train Model and AutoML use pipeline builder with different custom_training parameter and execution_type = automl
calls action submitOptimizationRequest with post request
project/${projectUuid}/sandbox-async/${pipelineUuid}/
Back-end
The endpoint
project/${projectUuid}/sandbox-async/${pipelineUuid}/
should also returnfeature_summary
To implement this we need to implement feature_summary calculation base on feature_table similarly to model's feature_summary
The endpoint
project/${projectUuid}/sandbox-async/${pipelineUuid}/
should also returnresult_type
fieldPerformance Implications
There are some extra calculation of feature_summary during get request
project/${projectUuid}/sandbox-async/${pipelineUuid}/
for pipelines with execution_type = pipelineDependencies
No 3rd party dependencies would be added
User Impact
Benefit: This feature simplifies experimentation, improves model training, and enhances user experience by enabling feature extraction directly within the UI.
Faster workflows: Extract and analyze features without running entire pipelines.
Optimized models: Focus models on relevant data aspects for potentially faster training and better performance.
Smoother development: Integrates seamlessly into UI fostering a streamlined workflow.
The text was updated successfully, but these errors were encountered: