MLflow Experiment Tracking

Description

MLflow is an open-source platform that tracks machine learning experiments by automatically logging parameters, metrics, models, and artifacts throughout the ML lifecycle. It provides a centralised repository for comparing different experimental runs, reproducing results, and managing model versions. Teams can track hyperparameters, evaluation metrics, model files, and execution environment details, creating a comprehensive audit trail that supports collaboration, reproducibility, and regulatory compliance across the entire machine learning development process.

Example Use Cases

Transparency

Tracking medical diagnosis model experiments across different hospitals, logging hyperparameters, performance metrics, and model artifacts to ensure reproducible research and enable regulatory audits of model development processes.

Documenting loan approval model experiments with complete parameter tracking and performance logging across demographic groups, supporting fair lending compliance by providing transparent records of model development and validation processes.

Reliability

Managing fraud detection model versions in production, tracking which specific model configuration and training data version is deployed, enabling quick rollback and performance comparison when system reliability issues arise.

Limitations

Requires teams to adopt disciplined logging practices and may introduce overhead to development workflows if not properly integrated into existing processes.
Storage costs can grow substantially with extensive artifact logging, especially for large models or high-frequency experimentation.
Tracking quality depends on developers consistently logging relevant information, with incomplete logging leading to gaps in experimental records.
Complex multi-stage pipelines may require custom instrumentation to capture dependencies and data flow relationships effectively.
Security and access control configurations require careful setup to protect sensitive model information and experimental data in shared environments.

Resources

MLflow Documentation

Documentation

Comprehensive official documentation covering MLflow setup, tracking APIs, model management, and deployment workflows with examples and best practices

mlflow/mlflow

Software Package

Official MLflow open-source repository containing the complete platform for ML experiment tracking, model management, and deployment

An MLOps Framework for Explainable Network Intrusion Detection with MLflow

Research Paper•Vincenzo Spadari et al.•Jun 26, 2024

Research paper demonstrating MLflow framework application for managing machine learning pipelines in network intrusion detection, covering experiment tracking, model deployment, and monitoring across security datasets

MLflow Tutorial - Machine Learning Lifecycle Management

Tutorial

Step-by-step tutorial demonstrating MLflow experiment tracking, model packaging, and deployment using real machine learning examples

Related Techniques

Name	Description	Assurance Goals
Model Pruning	Model pruning systematically removes less important weights, neurons, or entire layers from neural networks to create smaller, more efficient models whilst maintaining performance. This process involves iterative removal based on importance criteria (weight magnitudes, gradient information, activation patterns) followed by fine-tuning. Pruning can be structured (removing entire neurons/channels) or unstructured (removing individual weights), with structured pruning providing greater computational benefits and interpretability through simplified architectures.	Explainability Reliability Safety
Ridge Regression Surrogates	This technique approximates a complex model by training a ridge regression (a linear model with L2 regularization) on the original model's predictions. The ridge regression serves as a global surrogate that balances fidelity and interpretability, capturing the main linear relationships that the complex model learned while ignoring noise due to regularization.	Explainability Transparency
Generalized Additive Models	An intrinsically interpretable modelling technique that extends linear models by allowing flexible, nonlinear relationships between individual features and the target whilst maintaining the additive structure that preserves transparency. Each feature's effect is modelled separately as a smooth function, visualised as a curve showing how the feature influences predictions across its range. GAMs achieve this through spline functions or other smoothing techniques that capture complex patterns in individual variables without interactions, making them particularly valuable for domains requiring both predictive accuracy and model interpretability.	Transparency Explainability Reliability
Intrinsically Interpretable Models	Intrinsically interpretable models are machine learning algorithms that are transparent by design, allowing users to understand their decision-making process without requiring additional explanation techniques. This category includes decision trees and rule lists (which use if-then logic), linear and logistic regression models (which use weighted feature combinations), and other simple algorithms where the model structure itself provides interpretability. These models prioritise transparency over complexity, making them ideal when stakeholder understanding and regulatory compliance are paramount.	Transparency Reliability
Model Cards	Model cards are standardised documentation frameworks that systematically document machine learning models through structured templates. The templates cover intended use cases, performance metrics across different demographic groups and operating conditions, training data characteristics, evaluation procedures, limitations, and ethical considerations. They serve as comprehensive technical specifications that enable informed model selection, prevent inappropriate deployment, support regulatory compliance, and facilitate fair assessment by providing transparent reporting of model capabilities and constraints across diverse populations and scenarios.	Transparency Fairness Safety
Anomaly Detection	Anomaly detection identifies unusual behaviours, inputs, or outputs that deviate significantly from established normal patterns using statistical, machine learning, or rule-based methods. Applied to AI/ML systems, it serves as a continuous monitoring mechanism that can flag unexpected model predictions, suspicious input patterns, data drift, adversarial attacks, or operational malfunctions. By establishing baselines of normal system behaviour and alerting when deviations exceed predefined thresholds, organisations can detect potential security threats, model degradation, fairness violations, or system failures before they cause significant harm.	Safety Reliability Fairness Security