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Develop, deploy, monitor and version all your machine learning assets and data in a single unified environment. Scale easily within an Kubernetes ecosystem and serve thousands of models in production.
Streamline your ML operations end-to-end with Hopsworks, enabling faster delivery of production-ready AI models with less friction and more automation.
What’s in the box?
- Continuous Integration and Delivery (CI/CD)
Accelerate your ML deployments through built-in CI/CD pipelines, reducing the time and effort needed to go from experimentation to production. - Version Control and Rollback for Data and Assets
Maintain full reproducibility of your experiments and deployments with comprehensive model versioning, data lineage tracking, and environment management. - Model Serving and Deployment
Deploy models seamlessly in batch or real-time modes, leveraging industry standards like KServe and TensorFlow Serving to ensure scalability, reliability, and performance. - Advanced Monitoring and Governance
Proactively monitor model performance, data drift, and operational health, supported by real-time alerts, logging, and robust access controls, ensuring compliance and reducing risks. - Scalable, Multi-cloud Infrastructure
Deploy flexibly across AWS, GCP, Azure, Kubernetes, OVHcloud, and Red Hat, scaling your infrastructure dynamically to meet any operational demand. - Optimized Resource Utilization
Optimize your compute resources, minimize infrastructure costs, and maximize GPU utilization across your organization through smart scheduling, quota management, and detailed usage reporting.
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