ScienceOps logo

Data Science Operations System

Building models is hard work. Deploying them shouldn’t be.

ScienceOps model deployment integrations and language support

Productionize models in minutes

Built for Data Scientists

Make R and Python models immediately accessible via standard REST API requests without recoding from their native language.

Easy Embedding

Embedding models into production apps is as simple as sending an autogenerated code snippet to your dev team. Embed predictive models in any application capable of making REST API requests.

Real-time or Batch

Make real-time predictions in low-latency applications or use batch mode for bulk offline scoring.

ScienceOps logo: a data science operations system for managing predictive and advanced decision-making APIs

Iterate and Improve

Model Versioning

ScienceOps ships with version control and tracking to facilitate organized collaboration. Instate a new model via a hot-switch or roll-back to a previous version with the click of a button.

Unit Testing

Ensure that your source code operates properly with automatic unit testing before deployment. Unit test new models without interfering with existing models running in production applications.

embed models into production systems
deploy, manage, scale advanced analytical routines

How it works

Use-case: Lending and Financial Services

Track Model Predictions

Prediction Analytics

Access traffic and summary statistics of models running in production. Pinpoint and resolve model errors with speed and precision.

Input/Output Auditing

Track and compare model inputs and predictions over time. Inspect individual model inputs and outputs for easy auditing.

Systems Monitoring

System Health Overview

Monitor the stability of your models and servers in real time. ScienceOps ships with a variety of system and model checks in one centralized view.

Graphite Integrations

In addition to monitoring your system within our software, ScienceOps also ships with a built-in Graphite integration for tracking server side metrics.

ScienceOps admin interface
ScienceOps Graphite integration

“With ScienceOps, our data science and risk analytics teams can test, deploy and monitor models in real time, so the feedback loop for retraining and fine tuning models is significantly faster.”

-Dmitrijs Lvovs, Risk Manager at VIA SMS Group

Scale with Demand

Clustered Architecture

ScienceOps runs on a clustered architecture composed of one Master server and any number of Worker servers.

Additional Workers

Worker servers host predictive models in-memory and run models in discrete run-time environments. Add servers to scale with demand at any time.

Model Replication

Effortlessly replicate models to accommodate seasonal or permanent increases in throughput.

Deploy Anywhere

ScienceOps can be installed in a VPC, on-premise or in a hosted Yhat environment.

With ScienceOps your models and your data stay secure on your servers, behind your firewall, and within your data center.

Deploy ScienceOps in a VPC, on-premise or in a hosted Yhat environment

Trusted Around the Globe

Conde Nast
Digital Reasoning
Education Advisory Board

Building models is hard work. Deploying them shouldn’t be.

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