Experiment Tracking

TECHNICAL DEFINITION

Experiment tracking is the systematic logging and management of machine learning experiment artifacts, including hyperparameter configurations, model architectures, datasets, code versions, and performance metrics, to facilitate reproducibility, comparison, and optimization of models.

BACKGROUND

Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, software code or other forms of data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts.

SYNONYMS & ALIASES

• ML experiment management
• Run tracking
• Model iteration tracking
• MLflow tracking

USAGE NOTE

Tools for experiment tracking are indispensable for iterating quickly and maintaining an organized record of ML development.

DEVELOPERS

Organizations developing technology related to Experiment Tracking.

• Weights & Biases

  Provides a developer-first MLOps platform that includes robust experiment tracking, model versioning, and dataset management, enabling engineers to log, visualize, and compare runs for machine learning models and prompt engineering experiments.

• MLflow

  An open-source platform that manages the end-to-end machine learning lifecycle, offering tools for experiment tracking, reproducible runs, model packaging, and model deployment.

• Comet ML

  An MLOps platform designed to track, compare, and optimize machine learning experiments, datasets, and models, aiding in the development and deployment of AI systems, including those involving prompt design.

• Neptune.ai

  A metadata store for MLOps that helps data scientists and ML engineers manage and track experiments, organize model artifacts, and collaborate on AI projects, supporting iterative prompt design and model training.

• ClearML

  An open-source MLOps platform that provides capabilities for experiment tracking, data management, and MLOps automation, helping teams manage and reproduce machine learning workflows from research to production.

• Amazon SageMaker Experiments

  Part of AWS's Amazon SageMaker service, it helps data scientists and machine learning engineers track, compare, and organize their machine learning experiments, streamlining the iterative process of model development and prompt tuning.

• Google Cloud Vertex AI Experiments

  Integrated into Google Cloud's Vertex AI platform, this service allows users to track and manage their machine learning experiments, including model training runs and prompt-based evaluations, to improve reproducibility and collaboration.

• Microsoft Azure Machine Learning

  Offers comprehensive MLOps capabilities, including experiment tracking, model management, and deployment, enabling AI engineers to log metrics, parameters, and artifacts for various machine learning workflows and prompt engineering iterations.