Serve
Build scalable ML workflows,
without the bottlenecks.
Engineering, data, and business priorities become bottlenecks when running modelsÂ
at scale in production, which results in wasted time, effort, and resources.
Effortlessly serve your ML models at scale
with advanced deployment patterns and intuitive user experience
with advanced deployment patterns and intuitive user experience
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Create safe environments to run ML models scale in production can be time-consuming and costly. Seldon allows you to take control of your staging and production environments’ resource consumption and meet your service level objectives within budget.
Smoothly deploy to production without risking quality
A recent report, “2021 Enterprise Trends in Machine Learning” cites that the time required to deploy an ML model is increasing dramatically. A staggering 64% of organizations take a month or longer to put their models into production.
Check out our guide on deploying machine learning models on Kubernetes!
Serve models faster and safer, at scale
Discover how Seldon can help you optimize your machine learning deployments.Â
Deploy and host multiple ML models more cost-effectively
- via multi-model serving with overcommit functionality
Easily understand your entire ML system holistically
- with extended inference graphs
Ensure flexible communication with inference servers
- optimized for popular ML frameworks and custom language wrappers
Save valuable time and enhance collaboration across the business
- by deploying ML models using enterprise APIs with SDK
Optimize ML models and inference workflows for peak performance
- with traffic splitting deployment strategies like canary and A/B testing
Achieve simpler and faster setup and deployments
- with model workflow and configuration wizards that decrease time-to-value
