voxxed days thesaloniki 2016 - machine learning for developers

Machine Learning for Developers

Danilo Poccia, Technical Evangelist @danilop

danilop

Credit: Gerry Cranham/Fox Photos/Getty Images http://www.telegraph.co.uk/travel/destinations/europe/united-kingdom/england/london/galleries/The-history-of-the-Tube-in-pictures-150-years-of-London-Underground/1939-ticket-examin/

Credit: Gerry Cranham/Fox Photos/Getty Images http://www.telegraph.co.uk/travel/destinations/europe/united-kingdom/england/london/galleries/The-history-of-the-Tube-in-pictures-150-years-of-London-Underground/1939-ticket-examin/

1939 London Underground

Batch

Report

Batch

Report

Real-time

Alerts

Batch

Report

Real-time

Alerts

Prediction

Forecast

Predictions

Data Predictions

ModelData Predictions

Model

Machine Learning

SupervisedLearning

Machine Learning

UnsupervisedLearning

The task of inferringa model

from labeledtraining data

The task of inferringa model

to describehidden structure

from unlabeled data

ReinforcementLearning

Performa certain goal in a

dynamic environment, without an explicit

“teacher”

Driving a vehicle

Playing a game against an opponent

Reinforce

ment

Learning

ClusteringUnsuperv

ised

Learning

ClusteringUnsuperv

ised

Learning

ClusteringUnsuperv

ised

Learning

Regression

Binary Classification

Multi-class Classification

Supervise

d

Learning

Validation

Supervise

d

Learning

Training from Labeled DataSuperv

ised

Learning

Training

Validation

70%

30%

Be Careful of OverfittingSuperv

ised

Learning

Be Careful of OverfittingSuperv

ised

Learning

Be Careful of OverfittingSuperv

ised

Learning

Better Model,Different Predictions

Supervise

d

Learning

Better ModelSuperv

ised

Learning

?Data Model

Amazon EMRwith Spark (MLib)

Data Model

<demo>...

</demo>

Recommender: An Analysis ofCollaborative Filtering Techniques

Christopher R. Aberger

http://stanford.io/28OR3XE

More Info

Amazon EMRwith Spark (MLib)

Data Model

Data Scientists“Scalability”

AmazonMachine Learning

(Amazon ML)

Data Model

AmazonMachine Learning

(Amazon ML)

Data Model

BatchPredictions

AmazonMachine Learning

(Amazon ML)

Data Model

BatchPredictions

Real-timePredictions

Binary Classification Multiclass Classification Regression

Logistic Regression(Logistic Loss

Function + SGD)

Multinomial Logistic Regression

(Multinomial Logistic Loss + SGD)

Linear Regression(Squared Loss

Function + SGD)

The optimization technique used in Amazon ML is online Stochastic Gradient Descent (SGD)

<demo>...

</demo>

AmazonMachine Learning

(Amazon ML)

Data Model

BatchPredictions

Real-timePredictions

What about Deep Learning?

Neural Networks

Perceptron

Layers

Perceptron

https://upload.wikimedia.org/wikipedia/commons/8/8c/Perceptron_moj.png https://upload.wikimedia.org/wikipedia/commons/thumb/f/f1/Logistic-sigmoid-vs-scaled-probit.svg/240px-Logistic-sigmoid-vs-scaled-probit.svg.png

NeuralNetwork

Architectures

http://www.asimovinstitute.org/neural-network-zoo/

http://www.asimovinstitute.org/neural-network-zoo/

http://www.asimovinstitute.org/neural-network-zoo/

Deep Scalable SparseTensor Network Engine

(DSSTNE)

Pronounced “Destiny”

An Amazon developed library for buildingDeep Learning (DL) Machine Learning (ML) models

https://github.com/amznlabs/amazon-dsstne

Open Source

DSSTNE features for production workloads

Multi-GPUScale

Training and prediction both scale out to use multiple GPUs, spreading out computation and storage in a model-parallel fashion for

each layer

LargeLayers

Model-parallel scaling enables larger networks than are possible with a single GPU

SparseData

DSSTNE is optimized for fast performance on sparse datasets. Custom GPU kernels

perform sparse computation on the GPU, without filling in lots of zeroes

First DSSTNE Benchmarks

https://medium.com/@scottlegrand/first-dsstne-benchmarks-tldr-almost-15x-faster-than-tensorflow-393dbeb80c0f

Amazon EC2 P2 Instances

Up to:• 16 NVIDIA K80 GPUs• 64 vCPUs 732 GiB of host memory• combined 192 GB of GPU memory• 40 thousand parallel processing cores• 70 teraflops (single precision)• over 23 teraflops (double precision).• GPUDirect™ for up to 16 GPUs

DSSTNEData Model

Let’s Build a “Smart” Mobile App

Real-timePredictions

AWSLambda

Function(s)

AmazonMachine Learning

Model

AmazonKinesisStream

AmazonRedshiftDatabase

Amazon S3Bucket

AmazonCognitoIdentity

Amazon SNSMobile Push

AmazonMobile

Analytics

AmazonKinesis

Firehose

“Smart”Mobile

App

AWSLambda

Function(s)

AmazonMachine Learning

Model

AmazonKinesisStream

AmazonRedshiftDatabase

Amazon S3Bucket

AmazonCognitoIdentity

Amazon SNSMobile Push

AmazonMobile

Analytics

AmazonKinesis

Firehose

“Smart”Mobile

App

Where arethe Servers?

Where arethe Servers?

Build Event-DrivenServerless Apps

And Focus on Your Idea

AWS CLI

AWS SDKs

Automate

Build Apps With Services,Not Servers

aws.amazon.com/free

Thank you

@danilop danilop