Introducing Reactive Machine Learning

Jeff Smith @jeffksmithjr

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nom nom, the data dogScala & Python Spark & Akka Couchbase Machine Learning

Reactive + Machine Learning

Machine Learning Systems

Traits of Reactive Systems

Responsive

Resilient

Elastic

Message-Driven

Reactive Strategies

Reactive Machine Learning

Reactive Machine Learning

Collecting Data

Machine Learning Systems

Uncertainty Interval

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Immutable Factscase class PreyReading(sensorId: Int, locationId: Int, timestamp: Long, animalsLowerBound: Double, animalsUpperBound: Double, percentZebras: Double)

implicit val preyReadingFormatter = Json.format[PreyReading]

Distributed Data Storage

Partition Tolerance

Distributed Data Storage

Immutable Factsval reading = PreyReading(36, 12, currentTimeMillis(), 12.0, 18.0, 0.60)

val setDoc = bucket.set[PreyReading](readingId(reading), reading)

Scaling with Distributed Databases

Almost Immutable Data Model{ "sensor_id": 123, "readings": [ { "sensor_id": 456, "timestamp": 1457403598289, "lower_bound": 12.0, "upper_bound": 18.0, "percent_zebras": 0.60 } ] }

Hot Key

Truly Immutable Data Model{ "sensor_id_reading_id": 123456, "timestamp": 1457403598289, "lower_bound": 12.0, "upper_bound": 18.0, "percent_zebras": 0.60 }

Generating Features

Machine Learning Systems

Feature Generation

Raw Data FeaturesFeature Generation Pipeline

Microblogging Data

Pipeline Failure

Raw Data FeaturesFeature Generation Pipeline

Raw Data FeaturesFeature Generation Pipeline

Supervising Feature Generation

Raw Data FeaturesFeature Generation Pipeline

Supervision

Original Features

object SquawkLength extends FeatureType[Int]

object Super extends LabelType[Boolean]

val originalFeatures: Set[FeatureType] = Set(SquawkLength)val label = Super

Basic Features

object PastSquawks extends FeatureType[Int]

val basicFeatures = originalFeatures + PastSquawks

More Features

object MobileSquawker extends FeatureType[Boolean]

val moreFeatures = basicFeatures + MobileSquawker

Feature Collections

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

Feature Collectionsval earlierCollection = FeatureCollection(101, earlier, basicFeatures, label)

val latestCollection = FeatureCollection(202, now, moreFeatures, label)

val featureCollections = sc.parallelize( Seq(earlierCollection, latestCollection))

Fallback Collections

val FallbackCollection = FeatureCollection(404, beginningOfTime, originalFeatures, label)

Fallback Collectionsdef 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}

Learning Models

Machine Learning Systems

Learning Models

Features ModelModel Learning Pipeline

Models of Love

Traits of Spark

Reactive Strategies in Spark

Data Preparationval 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))

Learning a Modelval 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))

Evolving Modeling Strategiesval randomForest = new RandomForestClassifier() .setLabelCol("indexedLabel") .setFeaturesCol("indexedFeatures")

val revisedPipeline = new Pipeline() .setStages(Array(labelIndexer, featureIndexer, randomForest, labelConverter))

Deep Models of Artistic Style

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):

Exposing a Serviceclass 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()

Encoding Model Types

object ModelType extends Enumeration { type ModelType = Value val VGG = Value("VGG") val I2V = Value("I2V") }

Encoding Valid Configurationcase 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)

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

Calling the Servicedef 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] }))}

Profiles with Style

Hybrid Model learning

Features ModelModel Learning Pipeline

Publishing Models

Machine Learning Systems

Publishing Models

Model Predictive ServicePublishing Process

Building Lineages

val rawData: RawData val featureSet: Set[FeatureType] val model: ClassificationModel val modelMetrics: BinaryClassificationMetrics

Predicting

Machine Learning Systems

Predicting

Client Predictive ServiceRequest> <Prediction

Models as Servicesdef predict(model: Features => Prediction) (features: Features): Future[Either[String, Prediction]] = { ??? }

Models as Servicesval routes = { pathPrefix("model") { (post & entity(as[PredictionRequest])) { context => complete { predict(model: Features => Prediction)(extractFeatures(context)) .map[ToResponseMarshallable] { case Right(prediction) => PredictionSummary(prediction) case Left(errorMessage) => BadRequest -> errorMessage } } } } }

Clojure Functions

Clojure Predictive Service

Predicting

Client Predictive ServiceRequest

Summary

Machine Learning Systems

Traits of Reactive Systems

Reactive Strategies

Reactive Machine Learning

For Later

manning.com reactivemachinelearning.com medium.com/data-engineering

M A N N I N G

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Jeff Smith @jeffksmithjr reactivemachinelearning.com

M A N N I N G

Jeff Smith