The document outlines a meeting presentation on PredictionIO, focusing on personalized recommendations and machine learning as a service. It provides steps on how to install, use, and deploy the PredictionIO engine, along with prerequisites and commands. The document includes technical details about data sources, algorithm serving, and model training.

1. Prediction IO
2016/06/01 (Wed.) @ Exma-Square Meeting
WeiYuan(@v123582)

2. Outline § the future of Picklete
§ Recommendation
§ PredcitonIO
§ how to start ?
§ how to use ?
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3. the future of Picklete

4. 4
Picklete

5. 5
Picklete

6. Recommendation

7. Recommendation § Personalized recommendation
§ Collaborative filtering
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9. PredcitonIO

10. ML as a
ServicesIntegrate everything of ML together nicely and
move from prototyping to production
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11. PredcitonIO Open Source
§ open source framework
§ for developers and data
scientists
§ querying via restful API
Machine Learning
§ event collection
§ algorithms deployment
§ evaluation
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12. Overview
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13. DASE
§ Data Sources
§ Algorithm
§ Serving
§ Evaluation
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14. DASE
§ Data Sources
§ Algorithm
§ Serving
§ Evaluation
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15. Arch.
§ Spark
§ Mlib
§ HBase
§ HDFS
§ Hadoop
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16. how to start ?

17. Prerequisites § Apache Hadoop 2.4.0
§ Apache Spark 1.3.0 for Hadoop 2.4
§ Java SE Development Kit 7
§ MySQL 5.1
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18. Installation
§ Quick Install
§ status
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$ bash -c "$(curl -s https://install.prediction.io/install.sh)"
$ PATH=$PATH:/home/yourname/PredictionIO/bin; export PATH
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$ pio status
### Return:
…
[INFO] [Console$] Your system is all ready to go.18

19. how to use ?

20. Step 1
§ Run PredictionIO
1 $ pio eventserver &
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21. Step 2
§ Create a new Engine from an Engine Template
use Universal Recommender for example
1 $ pio template get <template-repo-path> ./<your-app-directory>
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22. Step 3
§ Generate an App ID and Access Key
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$ cd MyRecommendation
$ pio app new MyRecommendation
### Return:
…
[INFO] [App$] ID: 1
[INFO] [App$] Access Key: ...
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23. Step 4
§ Collecting Data
§ Sample Data
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$ curl <sample_data> --create-dirs -o data/<sample_data>
$ python data/import_eventserver.py --access_key <access-key>
./data/<sample_data>
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0::2::3
0::3::1
3::9::4
6::9::1
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24. Step 5
§ Deploy the Engine as a Service
./Engine.json
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…
"datasource": {
"params" : {
"appName": MyRecommendation
}
},
…
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25. Step 5
§ Bulid and Training the Predictive Model
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$ pio build
### Return:
…
[INFO] [Console$] Your engine is ready for training.
$ pio train
### Return:
…
[INFO] [CoreWorkflow$] Training completed successfully.25

26. Step 5
§ Bulid and Training the Predictive Model
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$ pio deploy
### Return:
…
[INFO] [HttpListener] Bound to /0.0.0.0:8000 [INFO]
[MasterActor] Bind successful. Ready to serve.
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27. Step 6
§ Use the Engine
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$ curl -d '{ "user": "1", "num": 2 }'
/ http://localhost:8000/queries.json
### Return:
{ "itemScores”: [
{ "item":"22", "score":4.072304374729956},
{ "item":"62", "score":4.058482414005789},
]
}27

28. Thanks for listening.
2016/06/01 (Wed.) @ Exma-Square
WeiYuan(@v123582)
v123582.github.io