1️⃣ User runs ML pipeline    python flow.py run             │             ▼ 2️⃣ Metaflow collects metadata    (run_id, step status, artifacts)             │             ▼ 3️⃣ Metaflow sends HTTP request using aiohttp    → Metadata Service API             │             ▼ 4️⃣ Metadata Service stores data    → asyncpg executes INSERT    → Data saved in PostgreSQL             │             ▼ 5️⃣ PostgreSQL sends NOTIFY event    → "metadata_updated"             │             ▼ 6️⃣ asyncpg (already LISTENing)    → Receives notification    → Executes Python callback             │             ▼ 7️⃣ Metadata Service sends    WebSocket message to UI             │             ▼ 8️⃣ Browser receives event    → UI updates instantly Is this the overall flow Metaflow uses to update info in Metaflow UI?

it's more like this