Favorita: MLflow Tracking
Stop losing track of which model run was best. This template wires MLflow into the Favorita forecasting work so every experiment's parameters, metrics, and artifacts are logged, compared, and reproducible.
Primary Outcome
A reproducible experiment-tracking setup where every model run's params, metrics, and artifacts are logged and comparable.
Solution
MLflow integration that logs every run's parameters, metrics, and artifacts, compares them, and promotes the winner through a model registry.
Deliverables
MLflow tracking wired into the training pipeline
Automatic logging of parameters, metrics, and model artifacts per run
A model registry pattern for promoting runs from staging to production
Run-comparison views to pick the best model on the metrics that matter
Reproducible run configs so any result can be regenerated
Strategic Context
Reproducibility is the unglamorous foundation of trustworthy ML. If you can't say exactly how a forecast was produced, you can't defend it when a stakeholder pushes back — and they will. Tracking turns model work from craft into engineering.
Technical Architecture
Reproducibility is the unglamorous foundation of trustworthy ML. If you can't say exactly how a forecast was produced, you can't defend it when a stakeholder pushes back — and they will. Tracking turns model work from craft into engineering.
Problem Statement
Without experiment tracking, model work becomes guesswork — runs blur together, the best result can't be reproduced, and nobody can explain how a given forecast was made.
Links
What's Included
MLflow tracking integration code, logging hooks for params/metrics/artifacts, model-registry setup, and example run-comparison and promotion scripts.
FAQs
Do I need a hosted MLflow server?
No — it runs locally to start. Move to a hosted tracking server when the team needs shared history.
Does it lock me into XGBoost?
No. MLflow is model-agnostic; the same hooks work for any framework.
Tech Stack
Tool 1
Tool 4
Tool 4
Tool 3
Tool 2
Tool 4
Primary Outcome
A reproducible experiment-tracking setup where every model run's params, metrics, and artifacts are logged and comparable.
Problem Statement
Without experiment tracking, model work becomes guesswork — runs blur together, the best result can't be reproduced, and nobody can explain how a given forecast was made.
Solution
MLflow integration that logs every run's parameters, metrics, and artifacts, compares them, and promotes the winner through a model registry.
Links
Deliverables
MLflow tracking wired into the training pipeline
Automatic logging of parameters, metrics, and model artifacts per run
A model registry pattern for promoting runs from staging to production
Run-comparison views to pick the best model on the metrics that matter
Reproducible run configs so any result can be regenerated
What's Included
MLflow tracking integration code, logging hooks for params/metrics/artifacts, model-registry setup, and example run-comparison and promotion scripts.
Strategic Context
Reproducibility is the unglamorous foundation of trustworthy ML. If you can't say exactly how a forecast was produced, you can't defend it when a stakeholder pushes back — and they will. Tracking turns model work from craft into engineering.
FAQs
Do I need a hosted MLflow server?
No — it runs locally to start. Move to a hosted tracking server when the team needs shared history.
Does it lock me into XGBoost?
No. MLflow is model-agnostic; the same hooks work for any framework.
Technical Architecture
Reproducibility is the unglamorous foundation of trustworthy ML. If you can't say exactly how a forecast was produced, you can't defend it when a stakeholder pushes back — and they will. Tracking turns model work from craft into engineering.
Tech Stack
Tool 1
Tool 4
Tool 4
Tool 3
Tool 2
Tool 4