Build scalable ML workflows,
without the bottlenecks.

Engineering, data, and business priorities become bottlenecks when running models 
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


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. 

Deploy and host multiple ML models more cost-effectively

Easily understand your entire ML system holistically

Ensure flexible communication with inference servers

Save valuable time and enhance collaboration across the business

Optimize ML models and inference workflows for peak performance

Achieve simpler and faster setup and deployments

Capital One reduced model deployment time from months to minutes

Model deployment can be hard work. The software and hardware infrastructure is often complex, meaning it can take months to see any results. Seldon drives cost savings through efficiency gains and advanced data science workflows. Our customers typically see their time-to-value drop significantly!

Capital One went from serving models in months to just minutes.

Seldon Technologies Limited, registered in England and Wales with company number 09188032

Registered Address:
45 Gresham Street, London, EC2V 7BG
United Kingdom