ML Inference Pipeline Generation from Training Artifacts
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Solution Overview
Problem
The existing machine learning (ML) process requires significant manual effort and computing resources due to the need for duplicating code between training and inference stages, leading to complex release processes and difficulties in debugging deviations in model performance.
Innovation Solution
A system that generates an ML inference pipeline by associating artifacts from the training stages with corresponding inference stages, allowing the reuse of training-time logic in the inference process, thereby reducing the need for separate inference code and minimizing customer intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If separate inference code is written manually, then inference pipeline can be deployed, but development time and complexity increase significantly
Solution Approach 1:
The system automatically copies training-time processing logic and artifacts to the inference pipeline. Instead of manually rewriting inference code, the system generates inference code by copying and adapting the training code, including data preprocessing steps, model loading logic, and post-processing routines, thereby eliminating duplicate manual coding effort
Solution Approach 2:
The system performs preliminary action by automatically generating the inference pipeline structure and code before deployment. The inference pipeline is pre-configured with all necessary components (preprocessing, model loading, prediction, postprocessing) based on the training pipeline configuration, so that when deployment is initiated, the code is already ready and requires minimal manual intervention
2Ease of operation
If manual orchestration of ML steps is performed, then ML process can be executed, but operation complexity increases
Solution Approach 1:
The system implements a universal orchestration mechanism that handles multiple ML operations (data loading, preprocessing, model training, evaluation, and inference) through a single automated framework. The same pipeline configuration language and execution engine used for training are also used for inference, providing a unified interface that simplifies operation while managing complex multi-step processes
3Productivity
If training artifacts are reused in inference, then deployment efficiency improves, but system complexity increases
Solution Approach 1:
The system merges the training pipeline configuration and inference pipeline configuration into a unified system. Training artifacts including processed data, preprocessing transformations, and model specifications are stored in a centralized artifact repository that both training and inference processes access. This consolidation allows automatic reuse of artifacts without requiring separate manual configuration for each phase
Data Source
AI summary
A system is disclosed that includes capabilities for generating a Machine Learning (ML) inference pipeline for deploying an ML model using artifacts received from one or more training stages in an ML training pipeline. The system receives one or more artifacts for one or more training stages in a set of training stages in a ML training pipeline associated with an ML process. The system then identifies one or more inference stages in an ML inference pipeline that correspond to the one or more training stages in the ML training pipeline. For each inference stage that corresponds to a training stage, the system associates the artifact received for the training stage with the inference stage. The system then generates the ML inference pipeline comprising the inference stages and their associated artifacts, where the artifacts include the artifacts received for the training stages in the ML training pipeline.