5 posts tagged with "AI/ML"

Gnok Studio now has an in-product Python notebook. You write Python against governed Gnok data, train a model, and register it so it's callable from SQL with ML_PREDICT — the whole train→serve loop, in one place, with no data movement and no separate notebook server to babysit.

This post builds a complete example end to end: read data, explore it, engineer features, train a scikit-learn pipeline, register it, and score new rows from SQL — then let Gnok AI write a cell for us.

SELECT * FROM reviews WHERE AI_FILTER('is a complaint about shipping', body).

If that line of SQL works, a whole class of "I just need to find the rows where X holds, and X isn't a regex" problems disappears. Gnok's AI_FILTER family lands the natural-language predicate in the place where you'd actually write it — alongside =, LIKE, and BETWEEN.

This post walks through the four new functions, the patterns they unlock, and how to keep cost predictable when LLMs are sitting in your hot path.

After watching ten thousand queries, Gnok has opinions about your workload. Learned scorers now feed the planner's partition and index advisors, so those opinions reflect the cost savings actually observed across your queries — not just the original heuristics.

The advisors run continuously as part of the service. This post walks through the surface that lets you see what they've learned: three SHOW … RECOMMENDATIONS commands that expose the live state of the join-order, materialized-view, and overall AutoML caches.

The AI/ML stack in Gnok has been usable for a while — register an ONNX model, build an HNSW index, materialize a feature group, run inference inline. What's new this month is that every track now holds up at production scale: vector search with WHERE clauses no longer falls back to brute-force, point-in-time training joins read durably from Iceberg time-travel, and the SQL editor knows which models you've actually registered.

This post walks through the three biggest changes — filtered ANN, offline-backed feature lookups, and the schema-aware ML autocomplete — with the SQL you can run today.

This tutorial builds a complete fraud detection system inside Gnok -- from data ingestion to ML scoring to alerting -- without any external services. We'll use COPY INTO for bulk loading, vector similarity for merchant profiling, statistical anomaly detection for flagging outliers, and natural language queries for ad-hoc investigation.