Hydrosphere Serving

Machine Learning Function as a Service (ML FaaS)
Run any model pipeline on any infrastructure.

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Products Overview / Hydrosphere Serving

Hydrosphere Serving is an opensource machine learning serving cluster for deploying machine learning models and ad-hoc algorithms. This enables you get your models up and running in an instant, on just about any infrastructure and using any of the available machine learning toolkits.

Serverless

Maximize abstraction from infrastructure, provisioning and management. Hydrosphere Serving deploys models eg. on Local, AWS, Kubernetes, Mesos and YARN environments.

Opensource

Everything is opensource. No premium features and enterprise versions. No hidden costs and learning curve can be as smooth as it gets.

No coding and no dependencies

Hydrosphere Serving needs just models metadata and runtime version to compose a deployable API instance.

ML Frameworks agnostic

Hydrosphere Serving cluster won’t lock you in to a specific machine learning framework, but lets you publish your data science toolbox by deploying scikit-learn, TensorFlow, mllib, deeplearning4j and other popular or custom machine learning runtimes in one click.

No training constraints

Flexible training of the data model within your favourite workspace (notebook) environment or through automatic training pipelines.

Native Spark ML support

Hydrosphere Serving natively supports Apache Spark prediction pipelines – no conversion to PMML is required.

Models versioning and A/B testing

Version control of predictive models included, to facilitate early and efficient comparative (A/B) testing.

Sidecar in action

Hydrosphere Serving has fully Sidecar compliant architecture to keep all runtime parameters (eg. networking) separated from the data science logic and component hierarchy.

Open Source

Hydrosphere Serving is open source software available under the Apache 2 License on GitHub

Hosted Version

Start from Hydrosphere Hosted and then move on premise any time