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yHat (How It Works (Scale With Demand (Clustered Architecture (ScienceOps…

- - - - access traffic and summary statistics of models running in production
      - pinpoint and resolve model errors with speed and precision
    - - track and compare model inputs and predictions over time
      - inspect individual model inputs and outputs for easy auditing
  - - - 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
    - - ScienceOps also ships with a built-in Graphite integration for tracking server side metrics
  - - - ScienceOps runs on a clustered architecture composed of one Master server and any number of Worker servers
    - - worker servers host predictive models in-memory and run models in discrete run-time environments. Add servers to scale with demand at any time
    - - Effortlessly replicate models to accommodate seasonal or permanent increases in throughput
- - - - make R and Python models immediately accessible via standard REST API requests without recoding from their native language
    - - embedding models into production apps is as simple as sending an auto-generated code snippet to your dev team
      - embed predictive models in any application capable of making REST API requests
    - - make real-time predictions in low-latency applications or use batch mode for bulk offline scoring