Automated Machine Learning Pipeline Deployment with Registry Validation
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Solution Overview
Problem
Conventional machine learning systems require laborious, time-consuming, and error-prone manual processes for designing, training, and deploying models, limiting their use and deployment due to the need for highly skilled data scientists and introducing delays and human errors.
Innovation Solution
A system and method for automated deployment of machine learning models, enabling self-serve instantiation of deployment and training pipelines based on simple definitions, reducing manual intervention and enabling continuous learning with automated updating and infrastructure reuse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used for designing, training, and deploying machine learning models, then highly skilled data scientists can perform complex operations, but the process becomes laborious, time-consuming, and error-prone
Solution Approach 1:
The system enables automated self-serve machine learning pipelines where the system automatically performs model training, validation, and deployment without requiring manual intervention at each step. The automated pipeline retrieves model definitions, validates them using test exemplars, instantiates inferencing pipelines, and deploys models automatically, eliminating the need for continuous human oversight while maintaining high accuracy and reducing deployment time.
2Adaptability or versatility
If manual operations are performed for each deployment step, then complex model architectures can be customized, but the complexity increases and requires highly-trained data scientists
Solution Approach 1:
The deployment process is segmented into distinct automated modules: model definition retrieval from registry, validation using test exemplars, pipeline instantiation, and deployment. Each module handles specific aspects of the complex process independently, allowing sophisticated model architectures to be processed through standardized automated steps without requiring manual complexity management.
Solution Approach 2:
An automated pipeline system acts as an intermediary between the model definitions and the deployment infrastructure. This intermediary automatically manages the complex transformations and validations required, shielding users from the underlying complexity while enabling sophisticated model deployments through simple requests.
3Productivity
If automated pipelines are implemented, then deployment speed increases and human error is reduced, but infrastructure complexity increases
Solution Approach 1:
The automated pipeline system serves multiple functions: it retrieves model definitions, validates models, instantiates pipelines, and deploys models. This multi-functional system consolidates what would otherwise require separate infrastructure components into a single unified automated pipeline that handles the entire deployment lifecycle, increasing speed while managing infrastructure complexity through consolidation.
Data Source
AI summary
Techniques for self-serve machine learning are provided. A request to deploy a machine learning model is received, where the request specifies whether to deploy the machine learning model for batch inferencing or real-time inferencing. In response to determining that a deployment pipeline for the machine learning model is not available, a deployment pipeline is instantiated for the machine learning model, comprising: retrieving a machine learning model definition from a registry containing trained machine learning model definitions, validating the machine learning model definition using one more test exemplars, and instantiating an inferencing pipeline including the machine learning model. Input data is processed using the inferencing pipeline.