Experiment Tracking with MLflow: Organizing ML Research

Every machine learning project involves running dozens, sometimes hundreds, of experiments. Different hyperparameters, different preprocessing steps, different model architectures, each producing a unique combination of metrics and artifacts. Without systematic tracking, the results of these experiments vanish into the fog of forgotten notebook outputs and overwritten files. Experiment tracking tools bring order to this chaos, and MLflow has emerged as the most widely adopted open-source solution.

The Case for Experiment Tracking

Without experiment tracking, data scientists face recurring problems. They cannot remember which combination of hyperparameters produced the best results from last week. They cannot reproduce a colleague's experiment because the exact configuration was never recorded. They cannot compare models systematically because metrics were logged in different formats across different notebooks.

Experiment tracking tools address all of these by automatically recording parameters (inputs to the experiment), metrics (outputs and performance measures), artifacts (model files, plots, data samples), and metadata (timestamps, code versions, environment details) for every experiment run.

"If you are not tracking your experiments, you are not doing science. You are doing alchemy: occasionally producing gold but unable to explain why or repeat it."

MLflow Components

MLflow Tracking

The core component of MLflow is the Tracking API, which logs parameters, metrics, and artifacts to a central store. Logging is simple: call mlflow.log_param() for hyperparameters, mlflow.log_metric() for performance metrics, and mlflow.log_artifact() for files like model checkpoints or evaluation plots. MLflow also provides autologging for popular frameworks. A single call to mlflow.pytorch.autolog() or mlflow.sklearn.autolog() automatically captures parameters, metrics, and models without manual logging code.

The MLflow Tracking UI provides a web interface for browsing experiments, comparing runs side by side, and visualizing metric trends across runs. You can search and filter runs based on parameters or metrics, making it easy to find the best-performing configurations.

MLflow Models

MLflow Models provides a standardized format for packaging models. An MLflow model includes the model artifact, a description of the model's flavor (PyTorch, scikit-learn, etc.), input/output schemas, and environment specifications (conda or pip requirements). This standardized packaging enables model deployment to various serving platforms without modification.

MLflow Model Registry

The Model Registry provides lifecycle management for models. Models are registered with a name, versioned automatically, and can transition through stages: Staging, Production, and Archived. This creates a clear governance process where models must be explicitly promoted to production, with full traceability of who promoted them and when.

MLflow Projects

MLflow Projects define a standard format for packaging ML code for reproducible runs. A project specifies its environment (conda, Docker), entry points (training scripts with parameters), and dependencies. Anyone can reproduce the project by running mlflow run with the project URL, and MLflow handles environment setup automatically.

Setting Up MLflow

Local Setup

The simplest way to start is running MLflow locally. Install with pip install mlflow, then start the tracking server with mlflow ui. By default, MLflow stores data in a local mlruns directory. This works for individual practitioners but does not support team collaboration.

Team Setup

For teams, deploy the MLflow tracking server with a database backend (PostgreSQL or MySQL for metadata) and an artifact store (S3, GCS, or Azure Blob Storage for model files and artifacts). This configuration enables multiple team members to log experiments to the same server and share results through the web UI.

Managed Options

Databricks provides a fully managed MLflow implementation integrated with its data and compute platform. Cloud providers offer their own managed tracking: SageMaker Experiments on AWS, Vertex AI Experiments on GCP, and Azure ML experiments. These managed services reduce operational overhead but may introduce vendor lock-in.

Alternatives to MLflow

Weights & Biases

Weights & Biases (W&B) offers a more polished UI, better visualization capabilities (especially for training curves and media logging), and collaborative features like report generation. W&B is a SaaS product with a free tier for individuals and academic researchers. Many researchers prefer W&B for its superior user experience, while MLflow is preferred for its open-source nature and self-hosting capabilities.

Neptune

Neptune provides experiment tracking with strong metadata management and collaboration features. Its custom dashboards and comparison views are particularly useful for teams running large-scale hyperparameter searches.

Comet

Comet offers experiment tracking with built-in data visualization, model explanation, and production monitoring. Its code diffing feature automatically captures the exact code that produced each experiment.

Best Practices

• Log everything from the start: It is easier to ignore logged information than to recreate it later. Log all hyperparameters, data versions, and environment details even if they seem unimportant now
• Use consistent naming: Establish conventions for experiment names, parameter names, and metric names. Consistency makes it possible to compare results across projects and team members
• Tag experiments meaningfully: Use tags to categorize experiments by project, approach, or purpose. Tags enable powerful filtering when you have hundreds of runs
• Automate logging: Use autologging where available and create helper functions for common logging patterns. The less friction in logging, the more consistently it will be done
• Version your data: Log a hash or version identifier for your training and evaluation datasets. Model performance is meaningless without knowing which data produced it

Experiment tracking is a foundational practice that pays dividends throughout the ML lifecycle. Whether you choose MLflow, Weights & Biases, or another tool, the important thing is to start tracking systematically from the beginning of every project.