Databricks Genie answers business questions in plain language, but its accuracy depends on the gold layer and semantics underneath. Here is how to build that layer, control the compute cost, and roll it out.
The exact fields an AI agent’s audit trail needs, from the human who directed it to the downstream action it took, and the lakehouse table that produces each one.
Why AI red teaming for agents and RAG depends on a governed gold-layer evaluation set with known-correct answers, and how to build one with taxonomies from NIST, OWASP and MITRE ATLAS.
A practical AI vendor risk assessment for mid-market teams: what to ask a vendor, how shadow AI changes procurement, and a copy-ready checklist tied to lakehouse controls.
Seven AI consulting firms for mid-market companies compared on delivery model, pricing and time to working software. Updated for 2026.
What an ISO/IEC 42001 AI management system looks like on a mid-market lakehouse: inventory, access, lineage, logging and incident handling in build order, and when certification is worth it.
The questions to ask an AI consulting firm, plus what a good answer and a red flag answer sound like for each one.
MLOps and AIML operations services that move models from laptop to production. See how the AIM-IT framework closes the lab-to-live gap.
A practitioner guide to AI and ML data analytics: real use cases, the modern MLOps stack, the challenges that stall projects, and