Description
Hydrosphere is an open-source MLOps platform that enables organizations to monitor, serve, and maintain machine learning models in production environments with enterprise-grade reliability and governance. The platform provides comprehensive tools for model versioning, A/B testing, monitoring for drift and performance degradation, and automated model retraining when quality thresholds are crossed. With its focus on the operational aspects of machine learning, Hydrosphere bridges the gap between data science experimentation and production AI applications, enabling organizations to deploy models confidently and maintain their performance over time as data and business conditions evolve.
Key Features
- Model serving infrastructure
- Automated drift detection
- Model performance monitoring
- A/B testing framework
- Model metadata management
Use Cases
- Production ML deployment
- Model governance automation
- ML infrastructure standardization
- Model performance optimization
- Continuous model delivery
Pricing Model
Open-source core with enterprise support
Integrations
CI/CD pipelines, Kubernetes clusters, Data science environments, Monitoring systems, Model development frameworks
Target Audience
MLOps engineers, Data scientists, DevOps teams, AI platform engineers, Enterprise ML practitioners
Launch Date
2016
Available On
Cloud deployment, Kubernetes environments, On-premises infrastructure
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