GG402 · Data Drift Detection
Identify statistical distribution drift in datasets with quantified metrics and recommendations.
What it does
Identify statistical distribution drift in datasets with quantified metrics and recommendations.
- Monitor ML model input drift in production
- Compare training and inference data distributions
- Trigger retraining pipelines on significant drift
Ideal buyer
MLOps engineers and data scientists monitoring production model health.
Run this through your governed agent wallet.
- 01Bootstrap AXON once with
npx @axon402/init. - 02Use the AXON runtime MCP tools to
search_x402_servicesorinspect_x402_offerfor this service. - 03Quote, test-buy, then run the governed paid fetch through AXON.
Send this
Prompt for your agent
A natural-language instruction for your LLM agent — with this endpoint exposed as a tool — to call this resource. Not sent to the endpoint; the endpoint consumes the JSON body below.
Pasting this prompt into a raw ChatGPT or unconfigured agent will notexecute the paid endpoint flow. Run it through an agent with the AXON runtime / MCP tools exposed (see “Use with AXON” above) so the 402 challenge, quote, and governed fetch are handled for you.
“Detect drift between my training dataset and this week's production data.”
Endpoint request body
The JSON payload your agent sends to the endpoint.
Advanced HTTP details
For integrators who need the raw protocol surface. Most agents should use AXON above instead of calling these directly.
Endpoint URL
curl fallback
curl https://gg402.vercel.app/data_drift_detection \ -H "Content-Type: application/json" \ -H "X-PAYMENT: [signed_payment_envelope]"
Payment & settlement details
Raw on-chain settlement parameters. AXON above handles these automatically through quote / test-buy / governed fetch.
Price & network
Trust & risk
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