We’re planning a 6-week upskilling sprint in Q1 to support a healthcare GPT launch, and I’m torn between NVIDIA DLI on inference optimization and Databricks’ GenAI/LLMOps track. For those sitting at the AI–GTM intersection, which programs sharpened positioning and improved POC-to-pilot conversion within 90 days? I’m after coursework that gives us harder levers for TCO, latency, and risk narratives — not just theory.
With a ‘6-week’ Q1 window for a healthcare GPT, I’d front‑load NVIDIA DLI for hard levers — TensorRT‑LLM, KV‑cache sizing, INT8/FP8, dynamic batching — to hit TCO/latency within 90 days, then tack on a short Databricks GenAI/LLMOps block to ship eval dashboards that move POC→pilot. , platform‑first training misses the squeeze; Databricks is great for SLAs and lineage, but it won’t wring inference like DLI — this combo cut our p95 about 35% and infra cost about 30% in 6 weeks. If you must pick one, go DLI first; link: https://www.nvidia.com/en-us/training/dli/ — any PHI/managed‑service constraints?
Split the 6 weeks: 3 on NVIDIA DLI, 3 on Databricks, but ground both in a TCO–latency harness using your actual healthcare RAG queries and PHI‑sanitized notes, then turn the results into a one‑page heatmap sales can drop into POCs. @kelsey_ross91 is right on hard levers; the kicker for conversion is wiring those wins into Databricks dashboards with p95 latency, cost per 1k tokens, and PMPM so buyers see “pilot‑ready” inside 90 days. Set a hard gate like p95 < 300 ms at 20 QPS and <$0.001/query — does that match your thresholds?