1
min read
Challenge
Pinterest’s ML platform supports more than 30,000 workloads a week, but its aging, customized experiment tracking system had become unreliable and difficult to upgrade. Outages and slow file operations were holding engineers back, while any migration had to protect the model deployments powering recommendations, search, and ads.
Solution
Pinterest chose CoreWeave Forge, using Weights & Biases Models to establish a unified MLOps platform for experiment tracking and model management, integrating it into both training and serving workflows. The team ran the old and new systems in parallel, validated models before deployment, and used built-in access controls alongside its own promotion checks to strengthen the path from experimentation to production.
Benefits
- Faster development: CoreWeave Forge skills help engineers create reports, evaluate models, and query metrics, making the platform easier to learn and use.
- Stronger production safeguards: Separate development and production access, team-specific registries, and promotion checks help Pinterest control model access and catch issues before deployment.
- Greater migration confidence: Model parity checks and readily available rollback builds help the team reduce risk as it moves use cases to CoreWeave Forge.
