07-production-monitoring

name: 07-production-monitoring description: > Use when setting up live quality monitoring for a deployed agent. Covers registered scorers with sampling, trace archival to Delta, metric backfill, and periodic human review — even if you just want "monitor my agent's safety score in production." Also use when adding conversation-level judges, tuning sampling rates, or building monitoring dashboards from UC trace data. SDLC Step 7. license: Apache-2.0 compatibility: "Requires Databricks workspace with MLflow 3.10+ and Unity Catalog. Scripts use uv." clients: [ide_cli, genie_code] bundle_resource: jobs deploy_verb: bundle_deploy deploy_note: "Live monitoring = MLflow registered scorers (SDK) + periodic trace-archival/backfill jobs deployed via the bundle deploy --target dev spine; archived traces land in the per-user prefixed schema. On Genie Code run job deploy/run through runDatabricksCli. See skills/genie-code-environment." coverage: full metadata: last_verified: "2026-06-05" volatility: high upstream_sources: [] author: "prashanth-subrahmanyam" version: "4.0.0" domain: "genai-agents" pipeline_position: "S7" consumes: "databricks_app, serving_endpoint" produces: "production_scorers, trace_archival_table, monitoring_dashboards" grounded_in: "https://docs.databricks.com/aws/en/mlflow3/genai/eval-monitor/production-monitoring, https://docs.databricks.com/aws/en/mlflow3/genai/eval-monitor/concepts/scorers, https://docs.databricks.com/aws/en/mlflow3/genai/eval-monitor/evaluate-conversations, https://docs.databricks.com/aws/en/mlflow3/genai/human-feedback/" upstream_sources: - name: "ai-dev-kit" repo: "databricks-solutions/ai-dev-kit" paths: - "databricks-skills/databricks-mlflow-evaluation/SKILL.md" relationship: "extended" last_synced: "2026-04-27" sync_commit: "281d9acd92d936bd5294f78bd7ec68fb12d4a696" fields_read:

• governance.scorer_suite.production_scorers
• governance.monitoring.required_alerts

Production Monitoring (MLflow 3.10+)

Continuous monitoring for GenAI agents after deployment: define → register → start scorers with sampling, optional stop / delete, trace archival to Unity Catalog, historical backfill, SQL dashboards, and periodic human labeling on production traces. Assumes the app logs traces (SDLC Step 6).

Upstream Lineage

This skill extends AI-Dev-Kit's databricks-mlflow-evaluation skill for UC trace ingestion, production monitoring, registered scorers, backfill, and trace analysis patterns. If monitoring APIs, UC trace table behavior, or scorer lifecycle semantics drift, consult the upstream skill first, then apply this skill's live-traffic sampling and dashboard conventions.

When to Use

• Enable live quality monitoring on production traffic (built-in or custom scorers).
• Persist traces to UC Delta for compliance, debugging, and trend SQL.
• Backfill scorers after policy or scorer code changes.
• Run multi-turn judges when sessions are tagged; calibrate automated scorers with expert labels.

For offline evaluation on fixed datasets before release, use the dev evaluation skill (Step 2). This skill is live traffic.

Trace Destination (Unity Catalog)

Bind the experiment to a UC OTEL location (MLflow 3.11+ on Databricks per Store MLflow traces in Unity Catalog). Prefer this supported SDK pattern instead of custom helpers such as _configure_uc_trace_destination() or a non-standard set_trace_destination shim:

import os
import mlflow
from mlflow.entities.trace_location import UnityCatalog

mlflow.set_tracking_uri("databricks")
os.environ["MLFLOW_TRACING_SQL_WAREHOUSE_ID"] = "<SQL_WAREHOUSE_ID>"

mlflow.set_experiment(
    experiment_name="/Users/you@example.com/my-genai-experiment",
    trace_location=UnityCatalog(
        catalog_name="my_catalog",
        schema_name="my_schema",
        table_prefix="my_prefix",  # optional; defaults if omitted
    ),
)

On older MLflow (for example 3.10), use mlflow.tracing.set_experiment_trace_location with UCSchemaLocation from mlflow.entities.trace_location instead—see MLflow release notes for your workspace. Grant the app identity USE on catalog/schema and SELECT/MODIFY on the *_otel_* tables.

Registered Scorer Lifecycle

Scorers are immutable: every .register(), .start(), .update(), .stop() returns a new handle—always reassign.

from mlflow.genai.scorers import Safety, ScorerSamplingConfig, delete_scorer, get_scorer

safety = Safety().register(name="prod_safety_check")
safety = safety.start(sampling_config=ScorerSamplingConfig(sample_rate=1.0))
# ... monitor ...
safety = safety.stop()
delete_scorer(name="prod_safety_check")

DO — reassign

s = Safety().register(name="prod_safety")
s = s.start(sampling_config=ScorerSamplingConfig(sample_rate=1.0))

DON'T — discard return value

s = Safety().register(name="prod_safety")
s.start(sampling_config=ScorerSamplingConfig(sample_rate=1.0))  # s still pre-start; .stop() unreliable

Tune sampling with .update(sampling_config=ScorerSamplingConfig(sample_rate=0.8)) (also returns a new instance).

Sampling Strategy (Best Practices)

Use ScorerSamplingConfig(sample_rate=...) (0.0–1.0). Official guidance: 1.0 for safety-critical; 0.05–0.2 for expensive LLM judges; moderate rates during iteration.

Add cheap heuristic scorers at higher rates; reserve LLM judges for samples.

Self-Contained Custom Scorers

Registered scorers are serialized for remote execution. They must not import your app package or reference module-level helpers.

Requirements (production monitoring):

• Only @scorer decorator functions (not class-based Scorer subclasses).
• All imports and helpers inside the function body (or inline literals).
• Avoid signature type hints that require imports (e.g. List from typing).
• Databricks recommends defining/registering from a notebook so serialization matches the monitoring environment.

DO — self-contained

from mlflow.genai.scorers import scorer, ScorerSamplingConfig

@scorer
def production_domain_check(inputs: dict, outputs: dict) -> float:
    import re

    text = str(outputs.get("response", ""))
    return 1.0 if re.search(r"\d", text) else 0.0

registered = production_domain_check.register(name="prod_numeric_hint")
registered = registered.start(sampling_config=ScorerSamplingConfig(sample_rate=0.2))

DON'T — project imports or outer scope

from my_app.validators import check_quality  # breaks on workers

@scorer
def bad_scorer(outputs):
    return check_quality(outputs)

Multi-Turn Production Judges & Conversation Context

Production supports multi-turn judges that evaluate full sessions (e.g. ConversationCompleteness, UserFrustration). Prerequisites:

• Set mlflow.trace.session on traces (same session ID per conversation). See Track users and sessions.
• Judges run after a session completion buffer (default: no new traces for that session for several minutes; configurable via MLFLOW_ONLINE_SCORING_DEFAULT_SESSION_COMPLETION_BUFFER_SECONDS on the monitoring job).

from mlflow.genai.scorers import (
    ConversationCompleteness,
    UserFrustration,
    ScorerSamplingConfig,
)

completeness = ConversationCompleteness().register(name="prod_completeness")
completeness = completeness.start(sampling_config=ScorerSamplingConfig(sample_rate=0.1))
frustration = UserFrustration().register(name="prod_frustration")
frustration = frustration.start(sampling_config=ScorerSamplingConfig(sample_rate=0.1))

Custom conversation judges (offline pattern): use make_judge with {{ conversation }} in instructions when evaluating traces/sessions—see Evaluate conversations.

Custom @scorer and span-level context: when the API allows, accept an optional trace argument so the scorer can inspect spans (not just last-turn inputs/outputs). Confirm the signature against your MLflow version’s custom-scorer docs; keep the body self-contained.

@scorer
def span_aware_check(inputs, outputs, trace=None):
    # Example: inspect trace/spans only if provided; keep logic serializable.
    if trace is None:
        return 0.5
    return 1.0

Unity Catalog Traces: Monitoring SQL Warehouse

When traces live in UC OTEL tables, bind a SQL warehouse so scorer results persist correctly:

from mlflow.tracing import set_databricks_monitoring_sql_warehouse_id

set_databricks_monitoring_sql_warehouse_id(
    sql_warehouse_id="<WAREHOUSE_ID>",
    experiment_id="<EXPERIMENT_ID>",
)

Call once at startup, after set_experiment / trace destination. Then register and .start() scorers as usual.

DON'T skip this when using UC trace storage—scorers may run without durable UC annotations.

Trace Archival (Delta)

Persist traces and assessments to a UC Delta table for long-term SQL and dashboards.

Use the mlflow.tracing entry point (not mlflow.tracing.archival):

import mlflow

mlflow.tracing.enable_databricks_trace_archival(
    delta_table_fullname="my_catalog.my_schema.archived_traces",
    experiment_id="YOUR_EXPERIMENT_ID",  # optional; defaults to active experiment
)

To stop exporting, use the disable API for your MLflow version (see Archive traces in Production monitoring); do not import enable from mlflow.tracing.archival.

DO enable once at process startup (same as trace destination / experiment setup).

DON'T call enable/disable per HTTP request.

Backfill Scorers

Retroactively score historical traces (e.g. after changing a judge). Prefer databricks.agents.scorers APIs as in the production monitoring guide.

Simple backfill (uses current registered scorers / defaults):

from databricks.agents.scorers import backfill_scorers

job_id = backfill_scorers(scorers=["prod_safety_check", "prod_numeric_hint"])

Custom sample rates and time window with BackfillScorerConfig:

from datetime import datetime
from databricks.agents.scorers import backfill_scorers, BackfillScorerConfig
from mlflow.genai.scorers import Safety, get_scorer

safety = get_scorer(name="prod_safety_check")

job_id = backfill_scorers(
    experiment_id="YOUR_EXPERIMENT_ID",
    scorers=[
        BackfillScorerConfig(scorer=safety, sample_rate=0.8),
    ],
    start_time=datetime(2026, 4, 1),
    end_time=datetime(2026, 4, 10),
)

DO verify names via list_scorers() and start with a short time range.

DON'T rely on typo’d scorer names—jobs may report scorers “not found”.

Manage Scorers

from mlflow.genai.scorers import list_scorers, get_scorer, delete_scorer

for s in list_scorers():
    print(s.name, s.sample_rate)

judge = get_scorer(name="prod_safety_check")
judge = judge.update(sampling_config=ScorerSamplingConfig(sample_rate=0.5))
delete_scorer(name="deprecated_scorer")

Namespace names by environment (prod_, staging_) to avoid collisions.

Unified Scorer Definitions

Use the same @scorer functions or built-in instances for:

1. Development — mlflow.genai.evaluate(...) on golden sets (Step 4).
2. Production — .register(name=...).start(sampling_config=...).

Production adds only registration, sampling, and experiment/model association—reduces drift between offline gates and live monitors.

Human Feedback in Production

Two complementary feedback streams land on production traces:

1. End-user feedback (👍/👎, ratings, comments) from the deployed app. Owned by 04c-end-user-feedback: mlflow.log_feedback(trace_id, name="user_feedback", source=AssessmentSource(HUMAN, source_id=user_id), ...). Surface feedback_rate and positive_rate on this skill's monitoring dashboards alongside scorer outputs; alert when they diverge from your automated quality signal.
2. Expert / SME labeling sessions on a sampled slice of production traffic. See Skill 04 (Evaluation Runs) → Human Feedback for label schemas (InputCategorical, InputText, InputTextList), create_labeling_session(), and session.sync().

Automated scorers drift; periodically pull production traces (especially those with negative end-user feedback) into labeling sessions so experts score a sample. The production twist: schedule sessions weekly or per-release, feed synced labels back into evaluation datasets, and re-run mlflow.genai.evaluate to align automated scorers with human judgment.

Monitoring Dashboards

Load references/monitoring-dashboard-queries.md if you need SQL queries for archival table dashboards (quality trends, errors, latency, alerts). Use relative windows (DATEADD), DESCRIBE the table, and join business dimensions when present in trace tags or payloads.

Layer views for: safety failures, rolling mean drops, volume anomalies, scorer-specific slices.

UC OTEL Dashboards & Export

When using UC OTEL storage, scorer outputs align with *_otel_metrics / *_otel_annotations (see trace UC docs). Load references/uc-otel-queries.md if you need span-level SQL for UC OTEL tables or export patterns.

Common Mistakes

Load references/common-mistakes.md if you encounter scorer registration failures, sampling issues, archival errors, or dashboard SQL problems.

DO / DON'T (Quick)

Validation Checklist

Scorers

• Every lifecycle call’s return value is assigned.
• Sampling: safety high; expensive judges lower; launch strategy documented.
• Custom scorers: inline imports, no project modules, serializable logic (numeric or structured return types per SDK docs).
• Scorer names namespaced by environment.
• At most 20 scorers per experiment (platform limit).

Traces & UC

• Experiment trace_location (Unity Catalog) aligned with deployment.
• set_databricks_monitoring_sql_warehouse_id set when UC OTEL monitoring is used.
• mlflow.tracing.enable_databricks_trace_archival(...) configured with UC grants.
• Dashboard SQL validated against actual columns.

Operations

• Backfill smoke-tested on a short window.
• Old scorers removed with delete_scorer when superseded.
• Archival table retention / OPTIMIZE policy defined.

Human loop

• Periodic labeling sessions from production samples (Skill 04 schemas).
• Labels feed judge tuning or eval datasets.

References

Databricks (canonical)

• Production monitoring
• Scorers & registered scorers
• Evaluate conversations
• Human feedback
• Trace storage / Unity Catalog
• Enable production monitoring with UC traces
• Label existing traces / labeling sessions

Related skills

• Step 4 — offline GenAI evaluation and human-feedback schemas.
• Steps 4–6 — tracing and deployment surfaces that feed this step.

Local reference files

Version History