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Experiment Tracking Concepts

Learn Experiment Tracking Concepts for free with explanations, exercises, and a quick test (for Machine Learning Engineer).

Published: January 1, 2026 | Updated: January 1, 2026

Table of Contents

Why this matters

As a Machine Learning Engineer, you make many small changes—hyperparameters, features, data slices, seeds, and code tweaks. Without systematic experiment tracking, you cannot answer basic questions like: What changed? Did it help? Can we reproduce it? Good tracking turns guesses into decisions and lets teams collaborate safely.

  • Real tasks: compare model versions across datasets, audit a production incident, hand over experiments to teammates, reproduce a result from months ago.
  • Benefits: faster iteration, fewer regressions, reliable handoffs, and clear evidence for stakeholders.

Concept explained simply

Experiment tracking is a structured logbook for ML work. Each run records what you tried, what happened, and the files produced.

Mental model

Think of a lab notebook: each page is a run. You write inputs (parameters, data version, code commit), observations (metrics), and attach artifacts (plots, models). You label pages so you can find and compare them later.

Core terms

  • Experiment: a collection of related runs (e.g., "ResNet sweep").
  • Run: one execution with a defined configuration and results.
  • Parameters (params): inputs you set (learning_rate, max_depth, seed, feature_set).
  • Metrics: outcomes you measure (accuracy, F1, AUC, loss).
  • Artifacts: files produced (model.pkl, confusion_matrix.png, requirements.txt).
  • Tags/Notes: labels and free text for filtering ("baseline", "ablation:drop_color").
  • Lineage/Context: code version (commit hash), data version/snapshot, environment (Python, libs), hardware.
  • Reproducibility: ability to rerun and obtain identical or statistically consistent results.

Minimal workflow to track reliably

  1. Create an experiment: choose a clear name and purpose.
  2. Define parameters: log all knobs you control (including seed and data split config).
  3. Log metrics: track per-epoch and final metrics; include standard deviation for multiple seeds.
  4. Save artifacts: save model, plots, and the exact training/validation indices if possible.
  5. Capture context: log git commit, data version/hash, and environment (package list).
  6. Tag and review: tag best candidates, add a short rationale note, and compare runs side by side.
What to log at minimum (practical checklist)
  • Params: model_type, learning_rate, batch_size, epochs, seed
  • Data: dataset_name, split_strategy, data_version or hash
  • Code: git_commit, script_name
  • Metrics: train_loss_last, val_metric_main (e.g., f1_macro), evaluation_timestamp
  • Artifacts: model file, metrics.csv, key plots
  • Environment: python_version, pip/conda freeze
  • Tags: purpose (baseline/sweep/ablation), owner, ticket or task id

Worked examples

1) Hyperparameter sweep for learning rate

Goal: find the best learning rate for a fixed architecture.

  • Experiment: "cnn_lr_sweep"
  • Params per run: learning_rate in [1e-4, 3e-4, 1e-3], seed=42, architecture=cnn_v3
  • Metrics: val_accuracy, val_loss (log per epoch + final)
  • Artifacts: loss_curve.png, best_model.pt
  • Tags: sweep, lr

Compare runs by final val_accuracy; cross-check if the best run also has a stable loss curve. Record a short note: "1e-3 diverged after epoch 8."

2) Ablation study: removing a feature group

Goal: understand contribution of color features.

  • Experiment: "ablation_color_features"
  • Params: feature_set=[all, no_color], seed in [0,1,2]
  • Metrics: f1_macro (report mean±std across seeds)
  • Artifacts: confusion_matrix.png per variant; table.csv summarizing seed runs
  • Tags: ablation

Decision: if mean F1 drops by >1.5 points when removing color, keep color features.

3) Data preprocessing change

Goal: switch normalization from per-feature to global.

  • Params: normalizer={per_feature|global}, seed=123, model=logreg
  • Context: data_version=v2025-05-01, git_commit=abc1234
  • Metrics: auc_pr, calibration_error
  • Artifacts: calibration_plot.png, model.pkl

Outcome: global normalization slightly improves calibration; note rationale and tag candidate for deployment.

Data and code lineage

Reproducibility depends on knowing exactly which code and data were used.

  • Code: record git commit and any uncommitted diffs summary.
  • Data: record dataset name, version, or content hash; for sampled/split data, save indices used.
  • Environment: record exact package versions and OS/CPU/GPU info.
Minimal lineage template you can paste into your run notes
{
  "git_commit": "",
  "script": "train.py",
  "data": {"name": "dataset_X", "version": "v1.2", "split_seed": 2024},
  "env": {"python": "3.10.12", "framework": "torch==2.2.1", "cuda": "12.1"}
}

Naming conventions and metadata

  • Experiment names: purpose_prefix + topic (e.g., "sweep_lr_cnn_v3").
  • Run names: short param key-values (e.g., "lr=0.001_seed=0").
  • Metric names: consistent, lowercase with underscores ("val_f1_macro").
  • Tags: lifecycle (baseline/sweep/ablation/candidate), area (data/model/eval), owner.
Quick naming do/don't
  • Do: val_f1_macro, test_auc_roc
  • Don't: F1, AUCtest (ambiguous)
  • Do: data_version=v2025-05-01
  • Don't: "latest"

Common mistakes and how to self-check

  • Not logging seed or split config. Self-check: can you recreate the exact train/val split?
  • Inconsistent metric naming. Self-check: can a script aggregate runs without manual renaming?
  • Forgetting data version. Self-check: is the dataset version/hash present for every run?
  • Manual copy-paste of results. Self-check: can the run be parsed programmatically from a single source?
  • Keeping only the best model. Self-check: are intermediate artifacts and learning curves preserved?
  • Mixing offline/online metrics. Self-check: label metrics with context (offline_cv, shadow_prod).
Self-audit mini checklist
  • Each run has params, metrics, artifacts, code, data, env, tags
  • Metric names are consistent across runs
  • There is a short note explaining purpose and outcome
  • Runs can be grouped and compared by a clear experiment name

Exercises

Use these to practice. A quick test is available at the end. Anyone can take it; login enables saving progress.

  • Exercise 1: Design a tracking plan for a new project (see details below).
  • Exercise 2: Convert messy logs into structured run records.
  • Exercise 3: Reproducibility drill: find missing lineage.
Exercise 1 details

Your team will train a gradient boosting model on tabular data with heavy feature engineering. Propose what to track under params, metrics, artifacts, and context. Include naming conventions and at least three useful tags.

Exercise 2 details

You receive a text log with intermixed prints. Normalize it into params, metrics (with steps), and artifacts entries. Identify any missing context you would add.

Exercise 3 details

You must reproduce a run that reports improved ROC AUC but no one can match it. Inspect a provided metadata snippet and list what is missing or ambiguous. Propose a corrected run record.

Practical projects

  • Personal benchmark: pick a small dataset and track a baseline, a sweep, and an ablation. Deliver a one-page summary with decisions.
  • Repro kit: a script that loads a run id and rebuilds the environment, data split, and model to re-evaluate test metrics.
  • Metric standardizer: a small utility that enforces naming and logs derived metrics (mean±std across seeds).

Mini challenge

In 20 minutes, design an experiment to compare three data augmentations. Define:

  • Experiment name and tags
  • Run params (including seed strategy)
  • Metrics to compare (primary and secondary)
  • Artifacts to save
  • Decision rule to pick a winner
Sample answer
experiment: aug_compare_v1
params: model=resnet18, aug in [flip, color_jitter, cutout], seeds=[0,1,2]
metrics: val_f1_macro (primary), val_accuracy, train_time
artifacts: confusion_matrix.png, loss_curve.png, best_model.pt
context: git_commit, data_version, env freeze
rule: select highest mean val_f1 with std <= 0.5; tie-break by accuracy

Who this is for

  • Machine Learning Engineers who want reliable, collaborative experimentation.
  • Data Scientists preparing results for production.
  • Students building repeatable ML projects.

Prerequisites

  • Basic ML training workflow knowledge (fit, evaluate, save model).
  • Comfort with Python and version control basics.
  • Understanding of common metrics for your problem type.

Learning path

  • Start: Experiment tracking concepts (this page).
  • Next: Data versioning and lineage in practice.
  • Then: Reproducible environments (conda/pip, containers).
  • Finally: Model registry and deployment promotion criteria.

Next steps

  • Complete the exercises below and check solutions.
  • Take the quick test to confirm understanding. Note: anyone can take it; sign in if you want your progress saved.
  • Apply tracking to one active project this week and run a small sweep with proper lineage.

Practice Exercises

3 exercises to complete

Instructions

Context: You will train a gradient boosting model with engineered features on a churn dataset.

  • List the exact fields you would log under params, metrics, artifacts, and context/lineage.
  • Propose consistent naming conventions for at least 5 metrics and 5 params.
  • Add at least 3 tags that help future filtering.
Expected Output
A structured list covering params, metrics, artifacts, and context with consistent names and at least three tags.

Experiment Tracking Concepts — Quick Test

Test your knowledge with 8 questions. Pass with 70% or higher.

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Model Registry Concepts

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