Auto-Deploying Machine Learning Components in Pipelined Search Queries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data analytics platforms face challenges in efficiently searching and analyzing massive quantities of diverse data across different programming languages and data sources, particularly with the integration of machine learning models and streaming algorithms, due to lack of frameworks that support cross-language deployment and reuse of ML-based pipelines.
Innovation Solution
The implementation of a data intake and query system that uses a late-binding schema and pipelined command language to process, index, and store data, allowing for flexible schema definition and extraction rules, enabling field-searchability and integration of machine learning components within the search workflow across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models and streaming algorithms are integrated into search workflows, then analytical capabilities are enhanced, but deployment complexity across different programming languages increases
Solution Approach 1:
The patent creates a universal deployment framework that allows machine learning models and streaming algorithms to be deployed across multiple programming languages (Python, R, Scala, Java) through a single interface. The system provides language-agnostic model serving capabilities, enabling analysts to use their preferred language while maintaining consistent ML pipeline execution and reuse across the platform.
Solution Approach 2:
The patent introduces an intermediary deployment service that acts as a mediator between diverse programming languages and machine learning model execution. This service handles model registration, versioning, and serving across different languages, eliminating the need for separate deployment mechanisms for each language and reducing overall deployment complexity.
2Loss of information
If machine learning models are trained and deployed within search workflows, then insights are improved, but the framework complexity for supporting multiple languages increases
Solution Approach 1:
The patent implements a universal ML pipeline framework that supports training, validation, and deployment of machine learning models across multiple programming languages through a unified interface. This allows analysts to leverage their preferred language (Python, R, Scala, or Java) while maintaining consistent model training workflows and reducing framework complexity through standardized operations.
3Productivity
If pipeline fragments are recovered and reused in future searches, then search efficiency is improved, but system complexity for managing reusable components increases
Solution Approach 1:
The patent implements preliminary action by automatically recovering and storing pipeline fragments (including ML model configurations, data processing steps, and transformation logic) from executed search queries. These fragments are cached and indexed for future reuse, eliminating the need to re-execute identical pipeline segments and improving search efficiency while the system manages complexity through automated fragment identification and storage.
Solution Approach 2:
The patent uses copying by creating reusable templates of pipeline fragments that can be instantiated multiple times across different search queries. Instead of managing complex original pipeline definitions, the system copies validated pipeline segments and stores them as reusable components, reducing system complexity through template-based management while enabling efficient reuse.
Data Source
AI summary
A method for deployment of machine-learning based operators within a query is described. For this embodiment, a sequence of operators associated with a query is identified, which includes at least a first operator and at least a second operator. The second operator is configured to perform operations, in accordance with a machine learning (ML) component, on data received as input from execution of the first operator. Schemas associated with the machine learning component is retrieved along with schemas associated with other operators within the sequence. Compatibility between at least an output schema associated with the first operator and an input schema associated with the second operator associated with the ML component is determined. Thereafter, a portion of the sequence of operators including at least the second operator and another operator of the sequence of operators successive to the second operator may be stored within a data store for subsequent use.