An interface between Wallaroo.ai and Equitus.us KGNn could be established by using Wallaroo.ai's workload orchestration features to process data from KGNn.
___________________________________________________________________________
Wallaroo.ai uses YAML (.yml) files for configuration and installation, and ZIP files containing instructions, requirements, and artifacts for its workload orchestrations. Equitus.us's KGNn platform automatically ingests, unifies, and contextualizes data into an "AI-ready knowledge graph."
A possible workflow for integration would be as follows:
Data Ingestion and Contextualization: Equitus.us's KGNn would ingest and process disparate data, structuring it into a knowledge graph.
Wallaroo.ai Workload Orchestration: A Wallaroo.ai workload orchestration, defined by a ZIP file, could contain a Python script that connects to the Equitus.us KGNn API. This script would fetch the "AI-ready" data for a specific inference task.
Inference Execution: The Wallaroo.ai inference engine would then execute the model on the data retrieved from KGNn.
Result Handling: The orchestration could be configured to send the inference results back to Equitus.us to be integrated into the knowledge graph, or to another specified destination.
Wallaroo.ai's API and SDK offer a lightweight way to make these connections, allowing for custom data sources like Equitus.us KGNn to be integrated into Wallaroo.ai's automated and scheduled ML workflows.
No comments:
Post a Comment