Scoring Tutorial Overview
Scoring is the act of applying a trained model to input data to produce an actionable result such as a categorization (fraud detection) or the next step in a pattern (retail product recommendation). Broadly interpreted, scoring could be something as sophisticated as applying a trained linear regression model or as straightforward as extracting the domain from an email address.
While it is possible to apply models as a batch process, there are two main reasons why you might want to do this in real time:
- Lazy evaluation saves time and processing. You only score those entries you read.
- Some information is available only in real time. For example, you may want to apply a different model based on the day of the week or the weather.
Scoring in a Kiji system is the real-time or on-demand application of a trained model to input data to produce an actionable result. KijiScoring makes these real-time, entity-centric calculations possible using a technique called freshening, which allows you to lazily apply a calculation to entity-centric data to produce scored results on read without launching an entire MapReduce job. You would identify a "freshness policy" that specified how you want the data to be freshened, such as always, never, or after a set interval. When your application attempts to read from a column that has a freshness policy and model associated with it, KijiScoring triggers the freshening process to run before the read is complete.
This tutorial shows a simple example of when real-time scoring excels over batch processing. It walks you through the steps to set up KijiScoring, building on the simple music recommendation scenario that the KijiMR Music Recommendation Tutorial implements. The Music Recommendation tutorial runs a batch process that recommends the best song based on what a user just listened to. Because it's a batch process, you're stuck with that recommendation until you run the batch process over again. In the time between when one song ends and the next song starts you may not have a valuable recommendation.
Modifying the scenario to use KijiScoring, you can defer the calculation to the last second. You can make a recommendation based on what the user just listened to, meaning they get the best recommendation every time.
The KijiScoring tutorial includes:
- Setting up the KijiScoring components on a machine that has access to the standalone Kiji environment where the data is stored in Kiji tables.
- Choosing a KijiFreshnessPolicy and ScoreFunction.
- Registering the KijiFreshnessPolicy and ScoreFunction to a Kiji table column.
- Accessing the refreshed column data via a
FreshKijiTableReader.
If you haven't already worked through the Music Recommendation Tutorial, you'll want to do that first.
KijiScoring Tutorial
- Scoring Tutorial Overview
- KijiScoring Setup
- Freshness Policy
- Score Function
- Registering a FreshnessPolicy and ScoreFunction
- Accessing the Refreshed Data
- Next Steps