Simon Chan, CEO of PredictionIO, presented on their open source machine learning server. PredictionIO has over 5,000 developers and powers over 200 applications. It allows users to easily collect data, train models, and retrieve predictions. Key challenges included coordinating workflows on distributed clusters and distributed in-memory model retrieval, both of which PredictionIO addresses through Apache Spark.

1. An Open Source Machine Learning Server
for Developers
@PredictionIO #PredictionIO
Simon Chan
simon@prediction.io

2. Thank you for having me here today!
• Simon Chan - CEO of PredictionIO
• A small team of Data Scientists and Engineers
• Mainly based in Silicon Valley, also London and Hong Kong

3. Top Github Open Source
• Over 5000 developers engaged
• Powering over 200 applications

4. Talk Focus:
• Machine Learning - A (Very) Brief Review
• Challenges We Face When Building PredictionIO

5. Machine Learning is Simple?

6. I am going to give an
example that will make
you… HUNGRY!

7. F FOOD Club – Menu
FOOD
CLUB

9. Coding time….
# Using PredictionIO
# Collect Data
cli = predictionio.EventClient("<my_app_id>")
cli.record_user_action_on_item("buy", "John", “BulgogiA")
# Predict top preferences
eng = predictionio.EngineClient("<my_engine_url>")
rec = eng.send_query({"uid" : "John", "n" : 5})

10. The Magic Behind: Engine
1. Data Sourcing and Preparation
2. Algorithm
3. Serving
4. Evaluation

13. Challenges and Solutions

14. Architectural Challenge 1
Workflow Co-ordination on a Distributed Cluster

15. Needs:
•Support multiple distributed engines
•Support multiple algorithms to execute in parallel
How to coordinate the workflow when you have
more pending tasks than processing units?

16. Attempt #1
Use a database system to store tasks, and
have a pool of workers pull tasks from it.
•Inefficient. Database becomes bottleneck
and potentially single point of failure.

17. Attempt #2
Use an Akka cluster.
Akka is a toolkit and runtime for building highly
concurrent, distributed, and fault tolerant event-driven
applications on the JVM.
•Fundamentally the same problem with the above.
•Need to build management suite on top.

18. Solution
Apache Spark: directed acyclic graph
(DAG) scheduling
Adapts to many different infrastructure:
Apache Spark standalone cluster, Apache
Hadoop 2 YARN, Apache Mesos.
Source: http://upload.wikimedia.org/wikipedia/commons/3/39/Directed_acyclic_graph_3.svg

19. Solution Source Code:
http://github.com/predictionio

20. Architectural Challenge 2
Distributed In-memory Model Retrieval

21. Needs:
•Engines produce models that are
distributed across a cluster.
Requires a way to serve these distributed
in-memory models to queries in real-time.

22. Solution
All PredictionIO engine instances are launched
inside a “SparkContext”.
A SparkContext represents the connection to a
Spark cluster, and can be used to create RDDs,
accumulators and broadcast variables on that
cluster.
Source: http://bighadoop.files.wordpress.com/2014/04/spark-architecture.png

23. •When an engine is local to a single
machine, it loads the model to its memory.
•When an engine is distributed,
SparkContext will automatically load the
model on a cluster.

24. Conceptual Code for the Solution
val sc = SparkContext(conf)
...
val model =
if (model_is_distributed) {
if (model_is_persisted) {
sc.objectFile(model_on_HDFS)
} else {
engine.algo.train()
}
} else {
...
}
}

25. PredictionIO 0.8

26. Built-in Engines:
•Item Recommendation
•Item Rank
•Item Similarity

27. Create an Engine Instance Project….
$ pio instance io.prediction.engines.itemrec
$ cd io.prediction.engines.itemrec
$ pio register

28. Collect Event Data….
cli = predictionio.EventClient("<app_id>")
cli.record_user_action_on_item("like", "John", “bulgogi_12”)
cli.record_user_action_on_item("view", "John", “bimbimbap_13”)

29. Configurate the Engine Instance settings
in params/datasource.json
{
"appId": <app_id>,
"actions": [
"view", "like", ...
], ...
}

30. Train the Data Model
$ pio train
Deploy the Engine Instance
$ pio deploy

31. Retrieve Prediction Results
from predictionio import EngineClient
client = EngineClient(url="http://localhost:8000")
prediction = client.send_query({"uid": "John", "n": 3})
print prediction
Output
{u'items': [{u'272': 9.929327011108398}, {u'313':
9.92607593536377}, {u’347': 9.92170524597168}]}

32. You can also….
• Change algorithm
• Tune algorithm parameter
• Compare and evaluate algorithm
• Add custom business logics

33. SDKs for:
• Python
• Ruby
• PHP
• Java / Andriod
• Scala
• Node.js
• iOS
• Meteor
• more….

34. Also,
build your own Engine!

35. Applications
of
Machine Learning
Speech Recognition
Personal Newsfeed
SPAM Filtering
Recommendation
Driverless Car
Churn Prediction
Ad Targeting
Fraud Detection
{

36. 감사합니다
Korean Documentation (Beta)!
http://docs.prediction.io/kr
- @PredictionIO
- prediction.io - Newsletters
- github.com/predictionio