Wallaroo.ai and Equitus.us could collaborate on IBM Power 11 by combining their respective strengths to create an end-to-end AI platform. Wallaroo.ai's expertise in AI model inference and MLOps (Machine Learning Operations) would be complemented by Equitus.us's focus on data preparation and real-time knowledge graphs. This synergy would provide customers with a comprehensive solution for building, deploying, and managing AI workflows on the secure and performant IBM Power 11 architecture.
Cutting Dev Time and Building AI Workflows
The partnership would address the challenge of repetitive "glue code" by offering a cohesive platform that integrates data processing, model deployment, and monitoring.
Declarative Pipelines: Wallaroo.ai's platform already enables the creation of declarative AI inference pipelines, which allows users to define the steps of their workflow (data preprocessing, model inference, post-processing) without writing custom code to connect them. Equitus.us would augment this by providing a declarative way to prepare and structure data, especially unstructured data, for Wallaroo's pipelines. This creates a seamless flow from raw data to a production-ready AI model.
AI-Ready Workflows: Equitus.us's Knowledge Graph Neural Network (KGNN) can ingest and unify disparate data sources, including batch, streaming, and unstructured data, and transform them into a knowledge graph. This pre-processed, contextualized data would then be fed directly into Wallaroo's platform. Wallaroo.ai would then handle the high-performance inference on this "AI-ready" data, allowing customers to build complex, real-time AI applications, such as Retrieval-Augmented Generation (RAG) systems.
Ensuring Pipeline Health
Wallaroo.ai's built-in monitoring and observability features would be enhanced by Equitus.us's data expertise, providing a more holistic view of the pipeline's health.
Built-in Data Quality Checks: Equitus.us would perform initial data validation and schema management during the data ingestion and knowledge graph creation phase. This would ensure that only high-quality, correctly formatted data enters the AI pipeline.
Schema Evolution and Error Handling: The combined platform could offer a robust system for handling schema evolution. As data sources change, Equitus.us would adapt the knowledge graph, while Wallaroo.ai's platform would ensure the downstream models and pipelines are not broken by these changes. Wallaroo.ai's model monitoring would provide real-time alerts for data and model drift, allowing teams to proactively address issues before they impact business outcomes.
Simplifying the Stack on IBM Power 11
The collaboration would provide a unified solution that leverages the specific capabilities of IBM Power 11 to simplify the customer's technology stack.
Consolidated Platform: Instead of having to integrate multiple disparate tools for data prep, model deployment, and monitoring, customers would have a single, unified platform. This reduces the number of vendors, simplifies licensing, and lowers the operational overhead of managing multiple systems.
Optimized Performance: Wallaroo.ai and Equitus.us are both optimized to run on IBM Power servers, which are known for their high performance and efficiency for AI workloads. By combining their software on this specific hardware, the joint solution can offer superior performance, lower costs (by reducing the need for GPUs), and high availability. This provides a "turnkey" solution that is optimized from the hardware to the application layer.
https://www.ibm.com/power/resources/isv/
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