feat(ml-service): add detailed ML observability metrics for inference, predictions, and errors
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@@ -1,4 +1,4 @@
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use prometheus::{Counter, Gauge, Histogram, HistogramOpts, Registry, TextEncoder};
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use prometheus::{Counter, CounterVec, Gauge, Histogram, HistogramOpts, HistogramVec, Opts, Registry, TextEncoder};
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use std::sync::OnceLock;
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use std::time::Instant;
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@@ -78,6 +78,76 @@ define_metric!(
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.expect("create gauge")
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);
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/// Per-class prediction counter
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define_metric!(
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predictions_by_class,
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CounterVec,
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CounterVec::new(
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Opts::new(
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"zeavis_ml_predictions_by_class_total",
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"Total predictions by predicted class label",
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),
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&["label"],
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)
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.expect("create counter_vec")
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);
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/// Per-class ground-truth counter (for monitoring label distribution)
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define_metric!(
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predictions_confidence,
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Histogram,
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Histogram::with_opts(
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HistogramOpts::new(
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"zeavis_ml_prediction_confidence",
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"Confidence values of predictions",
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)
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.buckets(vec![0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.85, 0.9, 0.95, 0.99, 1.0]),
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)
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.expect("create histogram")
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);
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/// Latency of ONNX inference (model.predict call)
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define_metric!(
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inference_duration_seconds,
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Histogram,
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Histogram::with_opts(
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HistogramOpts::new(
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"zeavis_ml_inference_duration_seconds",
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"ONNX model inference duration in seconds",
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)
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.buckets(vec![0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.5, 0.75, 1.0, 2.0]),
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)
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.expect("create histogram")
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);
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/// Image size processed by the ML service
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define_metric!(
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image_size_bytes,
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Histogram,
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Histogram::with_opts(
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HistogramOpts::new(
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"zeavis_ml_image_size_bytes",
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"Size of images sent for prediction in bytes",
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)
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.buckets(vec![1024.0, 10240.0, 51200.0, 102400.0, 204800.0, 512000.0, 1048576.0, 2097152.0]),
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)
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.expect("create histogram")
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);
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/// Error counter by error kind (e.g. bad_request, model_error, internal)
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define_metric!(
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errors_total,
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CounterVec,
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CounterVec::new(
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Opts::new(
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"zeavis_ml_errors_total",
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"Total errors by kind",
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),
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&["kind"],
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)
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.expect("create counter_vec")
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);
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// ── Request Guard (Drop-based cleanup for active gauge) ─
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pub struct RequestMetricsGuard {
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