Presented at the Open Source Analytics-New York Meetup on 5/11/16

1. Spark for Reactive Machine Learning:
Building Intelligent Agents at Scale
Jeff Smith
@jeffksmithjr

2. x.ai is a personal assistant
who schedules meetings for you

3. Agents

4. Agents
Autonomous
Goal-oriented
Capable of learning
Reactive

5. Amy Ingram
x.ai/how-it-works/

6. Agents
Autonomous
Goal-oriented
Capable of learning
Reactive

8. Tech Stacks

9. You

10. Scala & Python
Spark
MongoDB
Machine Learning

11. nom nom, the data dog
Scala & Python
Spark & Akka
Couchbase
Machine Learning

12. Reactive + Machine Learning

13. Machine Learning Systems

14. Traits of Reactive Systems

15. Responsive

16. Resilient

17. Elastic

18. Message-Driven

19. Reactive Strategies

20. Reactive Machine Learning

21. Reactive Machine Learning

22. Generating Features

23. Machine Learning Systems

24. Feature Generation
Raw Data FeaturesFeature Generation Pipeline

25. Microblogging Data

26. Pipeline Failure
Raw Data FeaturesFeature Generation Pipeline
Raw Data FeaturesFeature Generation Pipeline

27. Supervising Feature Generation
Raw Data FeaturesFeature Generation Pipeline
Supervision

28. Original Features
object SquawkLength extends FeatureType[Int]
object Super extends LabelType[Boolean]
val originalFeatures: Set[FeatureType] = Set(SquawkLength)
val label = Super

29. Basic Features
object PastSquawks extends FeatureType[Int]
val basicFeatures = originalFeatures + PastSquawks

30. More Features
object MobileSquawker extends FeatureType[Boolean]
val moreFeatures = basicFeatures + MobileSquawker

31. Feature Collections
case class FeatureCollection(id: Int,
createdAt: DateTime,
features: Set[_ <: FeatureType[_]],
label: LabelType[_])

32. Feature Collections
val earlierCollection = FeatureCollection(101,
earlier,
basicFeatures,
label)
val latestCollection = FeatureCollection(202,
now,
moreFeatures,
label)
val featureCollections = sc.parallelize(
Seq(earlierCollection, latestCollection))

33. Fallback Collections
val FallbackCollection = FeatureCollection(404,
beginningOfTime,
originalFeatures,
label)

34. Fallback Collections
def validCollection(collections: RDD[FeatureCollection],
invalidFeatures: Set[FeatureType[_]]) = {
val validCollections = collections.filter(
fc => !fc.features
.exists(invalidFeatures.contains))
.sortBy(collection => collection.id)
if (validCollections.count() > 0) {
validCollections.first()
} else
FallbackCollection
}

35. Learning Models

36. Machine Learning Systems

37. Learning Models
Features ModelModel Learning Pipeline

38. Models of Love

39. Data Preparation
val labelIndexer = new StringIndexer()
.setInputCol("label")
.setOutputCol("indexedLabel")
.fit(instances)
val featureIndexer = new VectorIndexer()
.setInputCol("features")
.setOutputCol("indexedFeatures")
.fit(instances)
val Array(trainingData, testingData) = instances.randomSplit(
Array(0.8, 0.2))

40. Learning a Model
val decisionTree = new DecisionTreeClassifier()
.setLabelCol("indexedLabel")
.setFeaturesCol("indexedFeatures")
val labelConverter = new IndexToString()
.setInputCol("prediction")
.setOutputCol("predictedLabel")
.setLabels(labelIndexer.labels)
val pipeline = new Pipeline()
.setStages(Array(labelIndexer, featureIndexer, decisionTree,
labelConverter))

41. Evolving Modeling Strategies
val randomForest = new RandomForestClassifier()
.setLabelCol("indexedLabel")
.setFeaturesCol("indexedFeatures")
val revisedPipeline = new Pipeline()
.setStages(Array(labelIndexer, featureIndexer, randomForest,
labelConverter))

42. Deep Models of Artistic Style

43. Refactoring Command Line Tools
> python neural-art-tf.py -m vgg -mp ./vgg -c ./images/
bear.jpg -s ./images/style.jpg -w 800
def produce_art(content_image_path, style_image_path,
model_path, model_type, width, alpha, beta, num_iters):

44. Exposing a Service
class NeuralServer(object):
def generate(self, content_image_path, style_image_path,
model_path, model_type, width, alpha, beta, num_iters):
produce_art(content_image_path, style_image_path,
model_path, model_type, width, alpha, beta, num_iters)
return True
daemon = Pyro4.Daemon()
ns = Pyro4.locateNS()
uri = daemon.register(NeuralServer)
ns.register("neuralserver", uri)
daemon.requestLoop()

45. Encoding Model Types
object ModelType extends Enumeration {
type ModelType = Value
val VGG = Value("VGG")
val I2V = Value("I2V")
}

46. Encoding Valid Configuration
case class JobConfiguration(contentPath: String,
stylePath: String,
modelPath: String,
modelType: ModelType,
width: Integer = 800,
alpha: java.lang.Double = 1.0,
beta: java.lang.Double = 200.0,
iterations: Integer = 5000)

47. Finding the Service
val ns = NameServerProxy.locateNS(null)
val remoteServer = new PyroProxy(ns.lookup("neuralserver"))

48. Calling the Service
def callServer(remoteServer: PyroProxy, jobConfiguration:
JobConfiguration) = {
Future.firstCompletedOf(
List(
timedOut,
Future {
remoteServer.call("generate",
jobConfiguration.contentPath,
jobConfiguration.stylePath,
jobConfiguration.modelPath,
jobConfiguration.modelType.toString,
jobConfiguration.width,
jobConfiguration.alpha,
jobConfiguration.beta,
jobConfiguration.iterations).asInstanceOf[Boolean]
}))}

49. Profiles with Style

50. Hybrid Model learning
Features ModelModel Learning Pipeline

51. Publishing Models

52. Machine Learning Systems

53. Publishing Models
Model
Predictive
Service
Publishing Process

54. Detecting Fraud
False Negative
False Positive

55. Model Metrics
val trainingSummary = model.summary
val binarySummary = trainingSummary
.asInstanceOf[BinaryLogisticRegressionSummary]
binarySummary.roc
roc.show()
binarySummary.areaUnderROC

56. Model Metrics
val predictions = model.transform(testingData)
val evaluator = new BinaryClassificationEvaluator()
.setLabelCol("label")
.setRawPredictionCol("rawPrediction")
.setMetricName("areaUnderPR")
val areaUnderPR = evaluator.evaluate(predictions)

57. Building Lineages
val rawData: RawData
val featureSet: Set[FeatureType]
val model: ClassificationModel
val modelMetrics: BinaryLogisticRegressionSummary

58. Summary

59. Agents
Autonomous
Goal-oriented
Capable of learning
Reactive

60. Machine Learning Systems

61. Traits of Reactive Systems

62. Reactive Strategies

63. Reactive Machine Learning

64. For Later

65. @jeffksmithjr
manning.com
reactivemachinelearning.com
medium.com/data-engineering
M A N N I N G
Jeff SmithUse the code opensanmu
for 40% off!

66. x.ai
@xdotai
hello@human.x.ai
New York, New York
We’re hiring!

67. Thank You!