This project has retired. For details please refer to its Attic page.

The code of an engine consists of D-A-S-E components:

[D] Data Source and Data Preparator

Data Source reads data from an input source and transforms it into a desired format. Data Preparator preprocesses the data and forwards it to the algorithm for model training.

[A] Algorithm

The Algorithm component includes the Machine Learning algorithm, and the settings of its parameters, determines how a predictive model is constructed.

[S] Serving

The Serving component takes prediction queries and returns prediction results. If the engine has multiple algorithms, Serving will combine the results into one. Additionally, business-specific logic can be added in Serving to further customize the final returned results.

[E] Evaluation Metrics

An Evaluation Metric quantifies prediction accuracy with a numerical score. It can be used for comparing algorithms or algorithm parameter settings.

Apache PredictionIO helps you modularize these components so you can build, for example, several Serving components for an Engine. You will be able to choose which one to be deployed when you create an Engine.

Engine Overview

The Roles of an Engine

The main functions of an engine are:

  • Train a model using the training data and be deployed as a web service
  • Respond to prediction query in real-time

An engine puts all DASE components into a deployable state by specifying:

  • One Data Source

  • One Data Preparator

  • One or more Algorithm(s)

  • One Serving

If more than one algorithm is specified, each of their model prediction results will be passed to Serving for ensembling.

Each Engine processes data and constructs predictive models independently. Therefore, every engine serves its own set of prediction results. For example, you may deploy two engines for your mobile application: one for recommending news to users and another one for suggesting new friends to users.

Training a Model - The DASE View

The following graph shows the workflow of DASE components when pio train is run.

Engine Overview

Respond to Prediction Query - The DASE View

The following graph shows the workflow of DASE components when a REST query is received by a deployed engine.

Engine Overview

Please see Implement DASE for DASE implementation details.

Please refer to following templates and their how-to guides for concrete examples.

Examples of DASE