ML Pipeline Code Generation With Template-Based Stage Assembly
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Solution Overview
Problem
Current mechanisms for generating codes for each stage in the data analytics cycle of Machine Learning (ML) models are manual and time-consuming, requiring significant effort and resources, especially due to the staggering number of permutations and combinations needed for different use cases.
Innovation Solution
A method and system that automatically generate codes for ML model operations by selecting predefined techniques based on user-defined criteria, generating dependency and environment configurations, and creating pipeline configurations for each stage, thereby reducing manual effort and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual code generation mechanisms are used for each stage in the data analytics pipeline, then code can be generated with some configurable options, but data scientists spend a considerable amount of time writing plumbing code and the process is not efficient
Solution Approach 1:
The system pre-defines multiple pipeline templates (e.g., classification, regression, NLP, computer vision) with pre-configured stages, techniques, and code structures. When a user selects a template and provides minimal input parameters, the system automatically generates the complete plumbing code for data preprocessing, feature engineering, model training, evaluation, and deployment. This preliminary preparation of code structures eliminates the need for data scientists to manually write repetitive plumbing code from scratch.
Solution Approach 2:
The code generation system is designed to be universally applicable across multiple machine learning use cases and pipeline configurations. A single system handles diverse scenarios including classification, regression, clustering, NLP, and computer vision tasks. The generated code adapts to different data types, models, and deployment environments through parameterized templates, making the solution multi-functional rather than requiring separate manual coding for each scenario.
2Adaptability or versatility
If code is manually written for each possible permutation and combination of the pipeline, then all use cases can be covered, but maximum amount of time and money is consumed in productionizing of the code
Solution Approach 1:
The system employs dynamic code generation where the pipeline structure, stages, and code logic adapt automatically based on user-selected parameters such as data type, target variable, performance metrics, and deployment environment. Instead of statically pre-writing code for every possible combination, the system dynamically constructs the appropriate pipeline configuration and generates corresponding code by combining predefined modules in real-time based on the specific use case requirements.
Solution Approach 2:
The machine learning pipeline is segmented into independent, modular stages (data loading, preprocessing, feature engineering, model training, evaluation, deployment) that can be independently configured and combined. Each stage has predefined techniques and code templates that can be selected and assembled based on the specific requirements. This segmentation allows the system to cover all permutations and combinations through modular assembly rather than requiring complete manual coding of every possible pipeline variation.
3Productivity
If automated code generation is implemented for all possible pipeline combinations, then efficiency increases, but the complexity of managing all permutations and combinations becomes staggering
Solution Approach 1:
The system performs preliminary organization of all possible pipeline permutations by creating predefined templates for common machine learning scenarios. Each template encapsulates a specific pipeline structure with predefined stages, techniques, and code patterns. This preliminary structuring reduces the management complexity by organizing the staggering number of permutations into manageable template categories, which can then be selected and customized without manually managing each individual permutation.
Solution Approach 2:
The system introduces an intermediary layer consisting of pipeline templates and configuration parameters that mediate between the user's high-level requirements and the complex underlying code generation process. Users interact with simplified template selections and parameter inputs rather than directly managing pipeline permutations. The intermediary template layer automatically handles the complexity of combining stages and techniques, translating user inputs into complete, production-ready code while shielding users from the underlying combinatorial complexity.
4Ease of operation
If manual mechanisms are used for generating code, then some configurable options are available, but the system does not allow for easy model training and deployment
Solution Approach 1:
The system pre-configures complete pipeline structures including data loading, preprocessing, feature engineering, model training, evaluation, and deployment stages with appropriate code templates and dependencies. When a user selects a pipeline template and provides minimal parameters (such as data source, target variable, and model type), the system automatically generates fully configured, production-ready code that can be immediately executed for model training and deployment without requiring manual configuration of each component.
Data Source
AI summary
A method for automatically generating codes to operate Machine Learning (ML) models disclosed. In some embodiments, the method includes operating, for each of a plurality of stages associated with ML model operations, at least one ML model. To operate each of the at least one ML model, the method may include selecting, for each of the at least one ML model at each of the plurality of stages, a predefined technique associated with a current stage; generating, for each of the at least one ML model at each of the plurality of stages, a code based on the selected predefined technique; generating, for each of the at least one ML model at each of the plurality of stages, a dependency configuration and an environment configuration for the generated code; and generating, for each of the at least one ML model, a pipeline configuration for execution of associated codes.