Palladium
  • Installation
    • Install from PyPI
    • Install from binstar
    • Install from source
  • Tutorial
    • Run the Iris example
    • Understand Iris’ config.py
  • Deployment
    • Web server installation
    • Building a Docker image with your Palladium application
    • Setup Palladium with Mesos / Marathon and Docker
    • Authorization
  • Web service
    • Predict
    • Alive
    • List
    • Fit, Update Model Cache, and Activate
  • Scripts
    • pld-fit: train models
    • pld-test: test models
    • pld-devserver: serve the web API
    • pld-stream: make predictions through stdin and stdout
    • pld-grid-search: find optimal hyperparameters
    • pld-list: list available models
    • pld-admin: administer available models
    • pld-version: display version number
    • pld-upgrade: upgrade database
  • Upgrading
    • Upgrading the database
    • Backward incompatibilities in code
  • R support
  • Julia support
  • Advanced configuration
    • Variables
    • Multiple configuration files
    • Avoiding duplication in your configuration
  • Frequently asked questions
    • How do I contribute to Palladium?
    • How do I configure where output is logged to?
    • How can I combine Palladium with my logging or monitoring solution?
    • How can I use Python 3 without messing up with my Python 2 projects?
    • Where can I find information if there are problems installing numpy, scipy, or scikit-learn?
    • How do I use a custom cross validation iterator in my grid search?
    • Can I use my cluster to run a hyperparameter search?
    • How can I use test Palladium components in a shell?
    • How can I access the active model in my code?
  • Related projects
  • palladium package
    • Submodules
    • palladium.R module
    • palladium.cache module
    • palladium.config module
    • palladium.dataset module
    • palladium.eval module
    • palladium.fit module
    • palladium.interfaces module
    • palladium.julia module
    • palladium.persistence module
    • palladium.server module
    • palladium.util module
    • palladium.wsgi module
    • Module contents
 
Palladium
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  • Related projects
  • Edit on GitHub

Related projectsΒΆ

There are a number of other interesting projects out there which have some features in common with Palladium. In the following, we will mention a selection.

  • DOMINO

    Infrastructure for data analysis (PaaS). A UI for running and examining experiments is provided. Experiments can be run in parallel and notification mechanisms can be set up. Models can be deployed as web services (referring to DOMINO’s documentation, the overhead for the HTTP server is about 150ms) and model updates can be scheduled. Supports Python, R, Julia, Octave, and SAS models. Commercial.

  • PredictionIO

    ML server based on Hadoop / Spark. Two engine templates for Apache Spark MLlib are provided for setting up a recommendation engine or a classification engine. It also allows for gathering new events. Supports Spark MLlib and Scala models (no support for Python, R, Julia). Open source.

  • Scikit-Learn Laboratory

    Tool to support experiments performed with scikit-learn. It allows to run various settings on different test sets and to get a summary of the test’s results. It does not aim at exposing models as web services. Supports Python models (no support for R or Julia). Open source.

  • yhat ScienceOps

    Platform for managing predictive models in production environments (PaaS). A command line tool and GUI are available for model management. It also provides a Load Balancer and can automatically scale the servers as needed. Supports Python and R models. Commercial.

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