Deep Learning Pipeline Configuration for End-to-End Model Building
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Solution Overview
Problem
Existing software applications are limited in their ability to facilitate the efficient configuration and deployment of data science models, particularly deep learning models, and do not provide the full range of functionalities needed for the end-to-end model creation process, including loading datasets, applying processing operations, training, and evaluating models.
Innovation Solution
A new functional subsystem is introduced that enables data scientists to configure and deploy deep learning-based pipelines, comprising components for data ingestion, transformation, model training, evaluation, and updating, using a network-accessible interface to streamline the model building process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing software applications are used for model creation, then basic functionality is provided, but the ability to facilitate efficient configuration and deployment of deep learning models is limited
Solution Approach 1:
The software application is designed to provide a comprehensive suite of functionalities within a single integrated platform, including data loading, preprocessing, model configuration, training, evaluation, and deployment. This multi-functional approach eliminates the need for multiple separate tools, thereby improving both productivity through streamlined workflows and adaptability through diverse supported operations.
Solution Approach 2:
The model creation process is divided into distinct modular stages (data ingestion, preprocessing, model configuration, training, evaluation, deployment), with each stage handled by specialized components. This segmentation allows users to efficiently configure and execute specific tasks while maintaining overall process coherence, addressing both efficiency and functional completeness requirements.
2Adaptability or versatility
If comprehensive functionalities are provided for end-to-end model creation, then model creation capability is improved, but software complexity increases
Solution Approach 1:
The software introduces intermediary components such as configuration files, standardized data interfaces, and automated workflow managers that mediate between user requirements and complex underlying processes. These intermediaries abstract complexity from users while enabling comprehensive end-to-end model creation capabilities.
Solution Approach 2:
The system incorporates automated functionalities including automatic data type detection, automatic preprocessing pipeline generation, and automatic model configuration based on data characteristics. These self-service features reduce the manual effort required to manage complex workflows while providing comprehensive model creation capabilities.
3Ease of operation
If manual configuration steps are required for each model creation task, then flexibility is maintained, but time and labor required for model creation increase
Solution Approach 1:
The software performs preliminary actions automatically, including automatic data loading from specified sources, automatic data type assignment, and automatic preprocessing based on predefined configurations. This eliminates manual configuration steps for routine tasks while maintaining flexibility for custom requirements, thereby reducing time and labor without sacrificing ease of operation.
Solution Approach 2:
The system automatically adjusts processing parameters based on data characteristics and model requirements, such as selecting appropriate preprocessing techniques, configuring model architecture parameters, and optimizing training hyperparameters. This automatic parameter adjustment simplifies operation while significantly reducing the time and labor required for model creation.
Data Source
AI summary
An example computing platform is configured to receive configuration data that defines a pipeline for building a deep learning model, the configuration data including data defining an input dataset, data type assignments for a set of input data variables included within the dataset, data transformations that are to be applied to the dataset, and a machine learning process that is to be utilized to train the deep learning model. Based on the received configuration data, the computing platform functions to build the deep learning model by obtaining the input dataset, assigning a data type to data in the dataset, selecting transformation operations for the data in the dataset, splitting the dataset into a sequence of data blocks, applying the transformation operations to each data block to produce a transformed dataset, generating a compressed data structure that includes the transformed datasets, and applying the machine learning process to the transformed datasets.