ML Lifecycle Management System for Scalable Model Deployment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine learning model lifecycle management systems face challenges in efficiently managing the execution and publication of machine learning model experiments and scores, leading to difficulties in ensuring the accuracy and reliability of models in production environments, which exhausts system resources and time.
Innovation Solution
A computing system with API circuitry that receives machine learning model selections and experiment creation inputs, determines execution engines, retrieves input data, executes experiments, and generates scores, allowing for the management of model lifecycles, including batch scoring and real-time scoring, while enabling data scientists to leverage scalable architectures for large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning model experiments are executed and scores are generated using existing mechanisms, then model scores are produced, but system resources are exhausted and deployment time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing input data, validating experiment configurations, and preparing execution environments before actual model scoring. The lifecycle management system orchestrates data retrieval, experiment setup, and resource allocation in advance, reducing the time required during actual deployment.
Solution Approach 2:
The model lifecycle is segmented into distinct phases: data retrieval, experiment execution, score generation, and result publication. Each phase is managed independently with dedicated circuitry and processes, allowing parallel processing and optimizing resource utilization across different stages of model deployment.
2Reliability
If machine learning model experiments are executed with multiple scoring files and data files, then comprehensive model scores are generated, but device complexity increases
Solution Approach 1:
The experiment execution circuitry is designed with multi-functionality to handle various experiment types (classification, regression, clustering) and multiple scoring scenarios simultaneously. The system can process different machine learning frameworks and algorithms through a unified interface, reducing the need for separate specialized systems for each model type.
Solution Approach 2:
The lifecycle management circuitry acts as an intermediary layer between data scientists and the complex execution infrastructure. It provides abstracted interfaces for experiment creation and management, shielding users from underlying system complexity while enabling comprehensive model evaluation through multiple scoring files and data sources.
Data Source
AI summary
Computing systems, computing apparatuses, computing methods, and computer program products are disclosed for machine learning model lifecycle management. An example computing method includes receiving a machine learning model selection, a machine learning model experiment creation input, a machine learning model experiment run type, and a machine learning model input data path. The example method further includes determining a machine learning model execution engine based on the machine learning model experiment creation input and the machine learning model experiment run type. The example method further includes retrieving input data based on the machine learning model input data path. The example method further includes executing a machine learning model experiment based on the machine learning model execution engine, machine learning model experiment creation input, and the input data. The example method further includes generating one or more machine learning model scores based on the machine learning model experiment.