voxxed days thesaloniki 2016 - machine learning for developers
TRANSCRIPT
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Machine Learning for Developers
Danilo Poccia, Technical Evangelist @danilop
danilop
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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/
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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
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Batch
Report
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Batch
Report
Real-time
Alerts
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Batch
Report
Real-time
Alerts
Prediction
Forecast
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Predictions
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Data Predictions
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ModelData Predictions
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Model
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Machine Learning
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SupervisedLearning
Machine Learning
UnsupervisedLearning
The task of inferringa model
from labeledtraining data
The task of inferringa model
to describehidden structure
from unlabeled data
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ReinforcementLearning
Performa certain goal in a
dynamic environment, without an explicit
“teacher”
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Driving a vehicle
Playing a game against an opponent
Reinforce
ment
Learning
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ClusteringUnsuperv
ised
Learning
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ClusteringUnsuperv
ised
Learning
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ClusteringUnsuperv
ised
Learning
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Regression
Binary Classification
Multi-class Classification
Supervise
d
Learning
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Validation
Supervise
d
Learning
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Training from Labeled DataSuperv
ised
Learning
Training
Validation
70%
30%
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Be Careful of OverfittingSuperv
ised
Learning
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Be Careful of OverfittingSuperv
ised
Learning
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Be Careful of OverfittingSuperv
ised
Learning
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Better Model,Different Predictions
Supervise
d
Learning
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Better ModelSuperv
ised
Learning
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?Data Model
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Amazon EMRwith Spark (MLib)
Data Model
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<demo>...
</demo>
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Recommender: An Analysis ofCollaborative Filtering Techniques
Christopher R. Aberger
http://stanford.io/28OR3XE
More Info
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Amazon EMRwith Spark (MLib)
Data Model
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Data Scientists“Scalability”
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AmazonMachine Learning
(Amazon ML)
Data Model
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AmazonMachine Learning
(Amazon ML)
Data Model
BatchPredictions
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AmazonMachine Learning
(Amazon ML)
Data Model
BatchPredictions
Real-timePredictions
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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)
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<demo>...
</demo>
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AmazonMachine Learning
(Amazon ML)
Data Model
BatchPredictions
Real-timePredictions
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What about Deep Learning?
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Neural Networks
Perceptron
Layers
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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
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NeuralNetwork
Architectures
http://www.asimovinstitute.org/neural-network-zoo/
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http://www.asimovinstitute.org/neural-network-zoo/
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http://www.asimovinstitute.org/neural-network-zoo/
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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
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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
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First DSSTNE Benchmarks
https://medium.com/@scottlegrand/first-dsstne-benchmarks-tldr-almost-15x-faster-than-tensorflow-393dbeb80c0f
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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
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DSSTNEData Model
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Let’s Build a “Smart” Mobile App
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Real-timePredictions
AWSLambda
Function(s)
AmazonMachine Learning
Model
AmazonKinesisStream
AmazonRedshiftDatabase
Amazon S3Bucket
AmazonCognitoIdentity
Amazon SNSMobile Push
AmazonMobile
Analytics
AmazonKinesis
Firehose
“Smart”Mobile
App
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AWSLambda
Function(s)
AmazonMachine Learning
Model
AmazonKinesisStream
AmazonRedshiftDatabase
Amazon S3Bucket
AmazonCognitoIdentity
Amazon SNSMobile Push
AmazonMobile
Analytics
AmazonKinesis
Firehose
“Smart”Mobile
App
Where arethe Servers?
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Where arethe Servers?
Build Event-DrivenServerless Apps
And Focus on Your Idea
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AWS CLI
AWS SDKs
Automate
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Build Apps With Services,Not Servers
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aws.amazon.com/free
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Thank you
@danilop danilop