See how W&B integrates with the ML ecosystem
Regardless of your preferred framework, environment, or workflow, our platform integrates with your existing tools and can be flexibly layered on top of any infrastructure.
Scott McClellan, Senior Director of Data Science and MLOps
NVIDIA
NVIDIA
“Putting AI into production requires enterprises to manage a broad range of processes, including data governance, experiment tracking, workload management, and compute orchestration. Pairing W&B with Run:ai MLOps software with NVIDIA accelerated systems and NVIDIA AI Enterprise software enables enterprises to effectively deploy intelligent applications that solve real-world business challenges.”
W&B integrates with a wide variety of tools across the ML lifecycle:
Data partners
- Netapp
- Activeloop
- NVIDIA
- Snowflake
- S3
- GCS
- Azure Blob Storage
Data stored in various data sources can be seamlessly logged in W&B for visualization & analysis.
Decisions made in W&B are pushed to data sources for actions (e.g. fix labeling errors, collect more data, transform data in a different way).
Training partners
- Anyscale
- SageMaker
- Run.ai
- NVIDIA
- Vertex AI
- Lambda
Training experiments can be easily logged in W&B for visualization & analysis
Decisions made in W&B are pushed to training environments for actions (e.g. train with different hyperparameters, change model architecture, retrain on different datasets)
Workflow partners
- Kubeflow
- Jenkins
- Airflow
- Github Actions
- Astronomer
Machine learning workload triggered by orchestration solutions can be easily logged in W&B for visualization and analysis.
Automation actions scheduled in W&B can be easily integrated into orchestration / CI/CD systems as part of the broader end-to-end workflow.
Production partners
- OctoML