| --- |
| title: Learning DASE |
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| |
| The code of an engine consists of D-A-S-E components: |
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| ### [D] Data Source and Data Preparator |
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| 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. |
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| ### [A] Algorithm |
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| The Algorithm component includes the Machine Learning algorithm, and the |
| settings of its parameters, determines how a predictive model is constructed. |
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| ### [S] Serving |
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| 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 |
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| An Evaluation Metric quantifies prediction accuracy with a numerical score. It |
| can be used for comparing algorithms or algorithm parameter settings. |
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| > 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. |
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|  |
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| ## The Roles of an Engine |
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| The main functions of an engine are: |
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| * Train a model using the training data and be deployed as a web service |
| * Respond to prediction query in real-time |
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| An engine puts all DASE components into a deployable state by specifying: |
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| * One Data Source |
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| * One Data Preparator |
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| * One or more Algorithm(s) |
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| * One Serving |
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| INFO: If more than one algorithm is specified, each of their model prediction |
| results will be passed to Serving for ensembling. |
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| 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. |
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| ### Training a Model - The DASE View |
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| The following graph shows the workflow of DASE components when `pio train` is run. |
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|  |
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| ### Respond to Prediction Query - The DASE View |
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| The following graph shows the workflow of DASE components when a REST query is received by a deployed engine. |
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|  |
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| Please see [Implement DASE](/customize/dase) for DASE implementation details. |
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| Please refer to following templates and their how-to guides for concrete examples. |
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| ## Examples of DASE |
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| - [DASE of Recommendation Template](/templates/recommendation/dase/) |
| - [DASE of Similar Product Template](/templates/similarproduct/dase/) |
| - [DASE of Classification Template](/templates/classification/dase/) |
| - [DASE of Lead Scoring Template](/templates/leadscoring/dase/) |
