MLflow for GenAI: Measuring and Monitoring AI Application Quality
Who this is for:
Architecture / Concept Overview: MLflow for GenAI: Measuring and Monitoring AI Application Quality
MLflow for GenAI adds tracing, GenAI-specific metrics, and evaluation pipelines to the existing MLflow stack.
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flowchart LR
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classDef governance fill:#5A3F52,stroke:#C28BB0,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
APP[GenAI Application] -->|Trace| TRACES[MLflow Traces]
APP -->|Evaluate| EVAL[MLflow Evaluate]
EVAL -->|Score| METRICS[GenAI Metrics]
METRICS -->|Judge| LLM_JUDGE[LLM-as-Judge]
TRACES -->|Store| EXP[MLflow Experiment]
METRICS -->|Store| EXP
EXP -->|Dashboard| UI[MLflow UI]
EXP -->|Alert| MON[Monitoring Alerts]
APP:::source
TRACES:::ingestion
EVAL:::processing
METRICS:::processing
LLM_JUDGE:::serving
EXP:::storage
UI:::governance
MON:::governance
*MLflow GenAI pipeline: applications emit traces, evaluations compute quality metrics using LLM judges, and results feed dashboards and alerts.*
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graph TD
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classDef governance fill:#5A3F52,stroke:#C28BB0,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
GENAI_METRICS[GenAI Metrics] --> RELEVANCE[Relevance]
GENAI_METRICS --> FAITHFULNESS[Faithfulness]
GENAI_METRICS --> GROUNDEDNESS[Groundedness]
GENAI_METRICS --> SAFETY[Safety]
GENAI_METRICS --> TOXICITY[Toxicity]
GENAI_METRICS --> CUSTOM[Custom Metrics]
RELEVANCE --> LLM_J[LLM-as-Judge]
FAITHFULNESS --> LLM_J
GROUNDEDNESS --> LLM_J
SAFETY --> HEURISTIC[Heuristic + LLM]
TOXICITY --> HEURISTIC
GENAI_METRICS:::governance
RELEVANCE:::processing
FAITHFULNESS:::processing
GROUNDEDNESS:::processing
SAFETY:::storage
TOXICITY:::storage
CUSTOM:::serving
LLM_J:::ingestion
HEURISTIC:::source
*GenAI metric taxonomy: LLM-as-judge metrics for quality and heuristic metrics for safety.*
Key Terms
Prerequisites and Setup
- MLflow (pre-installed on Databricks ML Runtime).
- Foundation Model APIs for LLM-as-judge evaluations.
- An evaluation dataset with questions and expected answers.
Step-by-Step Implementation
Configuration Reference
| Parameter | Default | Description |
|---|---|---|
model_type | — | Evaluation type: question-answering, text |
targets | — | Column with expected answers |
predictions | — | Column with generated answers |
evaluator_config.col_mapping | {} | Map custom column names to metric inputs |
model (custom metric) | — | LLM endpoint for judging |
temperature (judge) | 0.0 | Temperature for the judge LLM |