Every data scientist has been here: you run 50 training experiments over two weeks. You tweak learning rates, architectures, data preprocessing. Results go into a spreadsheet, a Slack message, or worse — nowhere. Three months later, your manager asks: “Can you reproduce the model we shipped?” And you can’t. You don’t remember which hyperparameters, which data version, which code commit produced that model. Experiment tracking solves this by automatically logging every training run with its parameters, metrics, code version, data version, and output artifacts.

A complete experiment record captures five things: (1) Parameters — hyperparameters, model architecture choices, preprocessing settings. (2) Metrics — training loss, validation accuracy, F1, latency, throughput — logged over time, not just final values. (3) Artifacts — model weights, plots, confusion matrices, sample predictions. (4) Code version — the exact Git commit (or notebook snapshot) that produced the run. (5) Environment — Python version, library versions, hardware (GPU type, memory). Missing any one of these makes reproduction unreliable.

MLflow (by Databricks) is the most widely adopted open-source experiment tracking platform. It has four components: MLflow Tracking (log parameters, metrics, artifacts), MLflow Projects (package code for reproducibility), MLflow Models (standard model packaging format), and MLflow Model Registry (stage and promote models). MLflow is free, self-hostable, and integrates with every major ML framework. Its API is straightforward: mlflow.log_param(), mlflow.log_metric(), mlflow.log_artifact(). The UI is functional but basic compared to W&B.

Weights & Biases is a SaaS-first experiment tracking platform with the best visualization UI in the industry. Key features: real-time dashboards with automatic training curve plots, W&B Sweeps for hyperparameter optimization (grid, random, Bayesian), W&B Tables for logging and comparing model predictions alongside inputs and ground truth, and W&B Artifacts for dataset and model versioning with full lineage tracking. The free tier is generous for individuals and small teams. The trade-off: it’s SaaS-first, so your data goes to W&B servers (a self-hosted option exists but requires significant infrastructure).

Choose MLflow if: you need self-hosting for data privacy, you’re in the Databricks ecosystem, you want full control over infrastructure, or budget is a hard constraint. Choose W&B if: you prioritize visualization and team collaboration, you want hyperparameter sweeps built in, you’re comfortable with SaaS, or you want minimal setup. Other options: Neptune.ai (strong metadata management), Comet ML (good code diffing), ClearML (open-source with orchestration). Many teams use both — MLflow for the model registry and W&B for visualization.

Hyperparameter tuning is a core part of experimentation. Grid search tries every combination — exhaustive but exponentially expensive. Random search (Bergstra & Bengio, 2012) samples randomly and is surprisingly effective because most hyperparameters have low “effective dimensionality” — only 1–2 matter most. Bayesian optimization builds a probabilistic model of the objective function and intelligently picks the next point to evaluate. Optuna (open-source) and W&B Sweeps are the most popular tools. For LLMs, hyperparameter tuning is less common — prompt engineering and RAG configuration are the new “tuning.”

Full reproducibility requires controlling five sources of randomness: (1) Random seeds — set seeds for Python, NumPy, PyTorch, and CUDA. (2) Data ordering — shuffle with a fixed seed; version the dataset. (3) Code version — pin to a Git commit. (4) Environment — use Docker or conda lock files to freeze all dependencies. (5) Hardware — GPU floating-point operations are non-deterministic by default; use torch.use_deterministic_algorithms(True) for exact reproducibility (at a performance cost). In practice, “close enough” reproducibility (within 0.1% of metrics) is usually sufficient.

1. Track from day one — don’t wait until you have “real” experiments. 2. Use autologging — MLflow and W&B both support automatic parameter/metric capture. 3. Tag experiments — add tags like “baseline,” “production,” “ablation” for easy filtering. 4. Log negative results — failed experiments are valuable data; they prevent others from repeating mistakes. 5. Review as a team — weekly experiment review meetings where the team looks at the tracking dashboard together. 6. Connect to CI — automated training runs should log to the same tracker as manual experiments.