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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