Weights & Biases
AIDeveloper-first platform to train, fine-tune, track, and manage machine learning and GenAI models from experiment to production.
-(0 Reviews)
Free Available
Starting from Free
Premium plans available
Weights & Biases
AI
Developer-first platform to train, fine-tune, track, and manage machine learning and GenAI models from experiment to production.
-(0 Reviews)
Free Available
Starting from Free
Premium plans available
About Weights & Biases
Weights & Biases is an AI developer platform that helps teams train and fine-tune models, track experiments, version datasets, and manage models from experimentation to production. It provides visualization and reporting tools to evaluate model performance, automate hyperparameter sweeps, and maintain reproducible ML workflows. The platform is designed for collaboration across teams, integrating with common ML frameworks and developer tools to accelerate model iteration and productionization of GenAI applications.
Key Features
Experiment Tracking: Record, visualize, and compare training runs with detailed metrics, system metrics, and custom charts to accelerate model iteration and debugging.
Model Management: Manage models through their lifecycle from experimentation to production, including versioning, metadata, and registry-style promotion of validated models.
Dataset Versioning: Track and version datasets and data lineage to ensure reproducibility and enable rollbacks and dataset comparisons across experiments.
Hyperparameter Sweeps Automation: Automate large-scale hyperparameter searches with orchestrated sweep runs and built-in analysis to find performant configurations faster.
Reporting and Evaluation: Generate reports and dashboards for model evaluation and stakeholder review, enabling consistent assessment of GenAI and LLM applications.
Integrations and SDKs: Integrate with common ML frameworks, CI/CD pipelines, and code repositories via SDKs and GitHub links to streamline developer workflows and collaboration.
Visualization Tools: Provide rich performance visualization components for comparing runs, inspecting metrics over time, and surfacing training anomalies.
Experiment tracking and visualization
Training and fine-tuning model workflows
Model lifecycle management from experimentation to production
Evaluation and tracking of Generative AI / LLM-powered applications
Dataset versioning and iteration tracking
Model performance visualization tools
Use Cases
Training and Fine-tuning Foundation Models: Track experiments and fine-tuning runs for large models, compare checkpoints, and manage model versions during iterative development.
Experiment Comparison and Debugging: Visualize and compare multiple training runs to identify regressions, diagnose performance issues, and iterate on model architectures or hyperparameters.
Reproducible Research and Dataset Lineage: Version datasets and record data provenance so teams can reproduce experiments, audit datasets, and manage dataset evolution.
Hyperparameter Optimization at Scale: Launch automated sweep jobs to explore hyperparameter spaces, analyze results, and select optimal configurations for production models.
Model Promotion and Productionization: Use model management tooling to promote validated experiments into production workflows and maintain traceability between experiments and deployed models.
Evaluating GenAI/LLM Applications: Track prompts, responses, and evaluation metrics for GenAI applications to measure quality, safety, and performance over time.
Cross-team Collaboration: Share dashboards, reports, and run metadata across data scientists and ML engineers to centralize knowledge and speed up handoffs.
Track and compare model training experiments across runs and teams
Version and iterate on datasets to reproduce experiments
Evaluate and monitor LLM and GenAI application performance
Manage model artifacts and promote models from experiments to production
Visualize model metrics and performance for debugging and reporting
Screenshots
+
