SQL Primitives for Hyperscale ML Model Orchestration
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Solution Overview
Problem
Managing and automating the orchestration of machine learning models in production is challenging, especially when dealing with thousands or millions of models, as existing tools and frameworks have mixed success and ease of use.
Innovation Solution
A method involving executing SQL queries with specific operators that utilize script and parameter relations to perform transformations, such as model instantiation, training, and scoring, using mappings defined in the SQL query's ON clause.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional tools and frameworks are used to manage machine learning models, then model orchestration can be performed, but scalability to thousands or millions of models is limited
Solution Approach 1:
The patent creates a universal SQL-based orchestration framework that can manage any machine learning model regardless of type, complexity, or scale. By using standard SQL operations (SELECT, JOIN, WHERE) as a common interface, the system achieves multi-functionality across diverse model orchestration tasks, enabling scalability from small to hyperscale deployments without requiring different tools for different scenarios.
Solution Approach 2:
The patent introduces SQL as an intermediary layer between the data foundation and model orchestration processes. This SQL interface acts as a mediator that translates high-level orchestration requirements into executable operations, enabling seamless management of models at any scale while maintaining compatibility with existing data infrastructure.
2Ease of manufacture
If multiple separate processes are used to manage model creation, training, scoring, and evaluation, then each process can be optimized independently, but the overall system complexity increases
Solution Approach 1:
The patent merges model creation, training, scoring, and evaluation processes into a unified SQL-based orchestration framework. By representing all these operations as SQL statements operating on relational data structures, the system reduces overall complexity while maintaining the ability to optimize individual processes through standard SQL capabilities like filtering, joining, and aggregation.
Solution Approach 2:
The patent applies homogeneity by treating all model orchestration operations uniformly through SQL statements. Whether creating, training, scoring, or evaluating models, the same SQL-based interface and data structures are used throughout, eliminating the need for different management approaches for different processes and thereby reducing system complexity.
3Adaptability or versatility
If custom orchestration frameworks are built to handle specific needs, then functionality can be tailored, but ease of operation and automation diminish
Solution Approach 1:
The patent enables self-service automation through SQL-based orchestration where standard SQL operations automatically manage model lifecycles. The system uses inherent SQL capabilities (automatic joins, filtering, aggregation) to handle orchestration tasks without requiring custom automation logic, making the system both easy to operate and highly adaptable through standard SQL syntax.
Solution Approach 2:
By using universal SQL operations that can represent any orchestration function, the system achieves both customization and ease of operation. The same SQL interface handles diverse orchestration needs while maintaining automatic execution, eliminating the trade-off between adaptability and operational simplicity.
Data Source
AI summary
A SQL query performs a function. The SQL query includes a SQL operator that has two input relations. The first input relation is a script relation having a plurality of script records. Each script record includes a transformation field, the contents of which specify a transformation to be performed by the SQL operator. The second input relation is a parameter relation having a plurality of parameter records. Each parameter record includes a data-to-process field that identifies data to be processed by the transformation specified in the transformation field of a selected script record. The selected script record is determined by a mapping. The SQL operator has one output relation having a plurality of output records. Each output record contains the result of transformation specified in a respective selected script record using the data to be processed identified in the data-to-be-processed field in a respective selected parameter record.