Connected, Repeatable Federated Pipelines with Trusted Integrations and Workflows
Proving federated AI works takes one team, one dataset, and a few hand-written scripts. Running it as a program takes much more: the same work repeated across sites and data refreshes, secure access to the data stores and models each site already uses, and a clear record of what ran and what resulted.
Trusted Integrations and Workflows, two new capabilities of the Rhino Federated Computing Platform (FCP), close that gap. Trusted Integrations open scoped, admin-approved connections between Rhino and the systems inside your secure perimeter. Workflows package steps across those systems into one repeatable, versioned process.
In this session we'll demonstrate a true-to-life federated project for exploring neurodiverse pediatric patient data for insights. Fueled by synthetic data from fictional pediatric clinics, we will show how running a Workflow pulls patient records from Snowflake and Databricks at different sites, uses Amazon Bedrock to flag sleep issues in clinical notes, and creates one cross-clinic answer using Rhino's federated analytics.
What you'll learn
- Why moving from a federated pilot to an enterprise federated program depends on governed connections and repeatable processes
- How Trusted Integrations securely connect Rhino to systems your teams already use, such as Snowflake and Amazon Bedrock
- How Workflows chain data retrieval, LLM processing, analysis, and other federated workloads across enterprise systems into one versioned, repeatable process
- How a single Workflow takes patient, medication, and clinical note data to a result: for each diagnosis group (ADHD, autism, or both), the medications linked to the fewest and the most documented sleep issues
