A server running multi-armed-bandit algorithm. A redis server is required to implement caching and delayed learning.
- As a consumer application,
- I want to know which feature to display,
- In order to display that feature.
- As an analytic guy,
- I want to get the feedback on the feature displayed,
- In order to improve the feature selection mechanism.
- As an analytic guy,
- I want to view the current stats,
- In order to know how the features perform.
$ curl http://localhost:9090/armsOutput:
{
"arm":2,
"id":"bcbl5jle4b3ipsuela90",
"created_at":"2018-06-06T03:19:42.336425107Z",
"updated_at":"2018-06-06T03:19:42.33642513Z"
}$ curl -X POST -d {"arm": 2, "id":"bcbl5jle4b3ipsuela90", "reward": 1} http://localhost:9090/armsOutput:
{"ok": true}$ curl http://localhost:9090/arms/stats
Output:
{
"counts":[0,0,1],
"rewards":[0,0,1],
"experiment_name":"colors",
"features":["red","green","blue"]
}- dockerize the service
- create a dashboard for the service (web ui)
- make the service configurable
- make the results of the service transparent (like feature X is n% better than feature Y)
- isolate configuration
- add labels and annotations for registry
- create sdk to integrate the functionality into other applications
- or make this into a sidecar proxy
- CRUD and API interface and CLI to integrate with the configuration
- fix the race issue that is found in the api
- load data from persistent store (e.g. redis)
- note that features might not be loaded in the correct order, hence it is important not to hardcode the values. use namespace instead