MLflow

Tauseef Fayyaz

A to Z

Glossary

Every concept in this sheet, plus the smaller terms it uses. Select a concept to open its card.

A

Artefacts: Output files attached to a run: plots, reports, feature lists, model files, sample predictions.
Autologging: mlflow.autolog() hooks into supported libraries such as scikit-learn, XGBoost, LightGBM, PyTorch Lightning and Keras, and logs parameters, metrics and the model when you call fit.

D

Dataset tracking: mlflow.data wraps a DataFrame or table with its source, name, digest and schema; log_input records it on the run with a context such as training or evaluation.

E

Experiments and runs: A run is one execution of training or evaluation code.

L

Library-specific autolog: Each integration also has its own autolog, such as mlflow.sklearn.autolog(), with options like log_models, log_datasets and disable.

M

Metrics: Numeric results logged over time.
MlflowClient: The lower-level API for reading and editing runs, experiments, metric histories and registered models by id.

N

Nested runs: Runs started with nested=True inside another run become its children, and the UI groups them under the parent.

O

Organising runs: Clear experiment names, meaningful run names and consistent tags, plus descriptions on experiments explaining the question being explored.

P

Parameters: Inputs to a run, such as hyperparameters and settings, stored as strings.

S

Searching runs: mlflow.search_runs returns a pandas DataFrame of runs matching a SQL-like filter on metrics, params, tags and attributes, in the order you ask.
System metrics: With log_system_metrics=True on a run (the psutil package is needed, plus nvidia-ml-py for GPUs), MLflow samples CPU, memory, disk and GPU use during training.

T

Tags: Free-form key-value labels on runs, experiments and models.
The MLflow UI: A web app listing experiments and runs, with metric charts, side-by-side run comparison, parallel coordinates plots, artefact browsing, models and traces.

W

What MLflow is: An open-source platform for the machine learning life cycle: tracking experiments, packaging models, a model registry, serving, evaluation, and tracing for GenAI apps.