BigML Spring 2016 Release

BigML Spring 2016 Release

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Spring 2016 Release

POUL PETERSEN (CIO)

Enter questions into chat box – we’ll answer some via chat; others at the end of the session

https://bigml.com/releases

ATAKAN CETINSOY, (VP Predictive Applications)

Resources

Moderator

Speaker

Contact [email protected]

Twitter @bigmlcom

Questions

@whizzml

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Promise of ML

time

Want

•Reduce churn

•Increase conversion

•Improve diagnosis

•Reduce fraud

•Etc.

Automated InsightsData

Have

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ML Hurdles

time

•Which algorithms? •How to scale it? •How to handle real data? •How to tune it? •How to automate it?

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Current Resources

SOURCE DATASET CORRELATIONSTATISTICAL

TEST

MODEL ENSEMBLELOGISTIC

REGRESSION EVALUATION

ANOMALY DETECTOR

ASSOCIATION DISCOVERY PREDICTION

BATCH PREDICTIONSCRIPT LIBRARY EXECUTION

Dat

a Ex

plo

ratio

nSu

per

vise

d

Lear

ning

Uns

uper

vise

d

Lear

ning

Aut

omat

ion

CLUSTER Scoring

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BigML Vision

time

Auto

mat

ion Paving the Path to Automatic Machine Learning

REST  API

Programmable  Infrastructure

A

Sauron  • Automatic  deployment  and  auto-­‐scaling

Data  Generation  and  Filtering

C

Flatline  • DSL  for  transformation  and  new  field  generation

B

Wintermute  • Distributed  Machine  Learning  Framework  

2011 Spring 2016

Automatic  Model  Selection

E

SMACdown    • Automatic  parameter  optimization

Workflow  Automation

D

WhizzML  • DSL  for  programmable  workflows  

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Workflow MapDecision  Trees  Bagging  Decision  Forest  LogisGc  Regression  

MODEL

DATASET

CLUSTER ANOMALY

ASSOCIATION

SOURCE

K-­‐Means  G-­‐Means  

IsolaGon  Forest  

Magnum  Opus  

StaGsGcal  Tests  CorrelaGons  

STATSDATASET

Flatline  Flatline  Editor  

PREDICTION

Batch  PredicGon  Batch  Anomaly  Batch  Centroid  EvaluaGon  

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Original Workflow

SOURCE DATASET MODEL PREDICTION

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Regular Workflows

MODEL

FILTERSOLD HOMES

BATCH PREDICTION

NEW FEATURES

DATASET DEALS DATASET

FILTERFORSALE HOMES NEW FEATURES

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Model Selection

ENSEMBLE LOGISTIC REGRESSION

EVALUATION

SOURCE DATASET

TRAINING

TEST

MODEL

EVALUATIONEVALUATION

CHOOSE

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Model Tuning

ENSEMBLE N=20

EVALUATION

SOURCE DATASET

TRAINING

TEST

EVALUATIONEVALUATION

ENSEMBLE N=10

ENSEMBLE N=1000

CHOOSE

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SMACdown

•How many models? •How many nodes? •Missing splits or not? •Number of random candidates? •Balance the objective?

SMACdown can tell you!

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Best-First Features{F1}

CHOOSE BEST S = {Fa}

{F2} {F3} {F4} Fn

S+{F1} S+{F2} S+{F3} S+{F4} S+{Fn-1}

CHOOSE BEST S = {Fa, Fb}

S+{F1} S+{F2} S+{F3} S+{F4} S+{Fn-1}

CHOOSE BEST S = {Fa, Fb, Fc}

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Stacked Generalization

ENSEMBLE LOGISTIC REGRESSION

SOURCE DATASET

MODEL

BATCH PREDICTION

BATCH PREDICTION

BATCH PREDICTION

EXTENDED DATASET

EXTENDED DATASET

EXTENDED DATASET

LOGISTIC REGRESSION

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Better Algorithms

•Stacked Generalization •Boosting

•Adaboost • Logitboost •Martingale Boosting •Gradient Boosting

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Why Workflows

•Machine Learning is iterative by nature. •ML tools still require many repetitive (and manual) tasks.

•Instead of helping to focus on the output many tools force analysts, developers, and scientists to focus on infrastructure, parallelism, etc.

•Not everybody can implement complex workflows or meta-algorithms but many people can reuse them.

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WhizzML Features

•A Domain-Specific Language (DSL) for automating Machine Learning workflows.

•Complete programming language.

•Machine Learning “operations” are first-class citizens.

•Scale is provided for free.

•API First! - Everything is composable.

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WhizzML API Resources

SCRIPT LIBRARY EXECUTION

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export BIGML_USERNAME=myuser export BIGML_API_KEY=6ef37b3d791061d345ef51281dae821ac7943ed7 export BIGML_AUTH="username=$BIGML_USERNAME;api_key=$BIGML_API_KEY"

export SCRIPT="https://bigml.io/script?$BIGML_AUTH" export LIBRARY="https://bigml.io/library?$BIGML_AUTH" export EXECUTION="https://bigml.io/execution?$BIGML_AUTH"

Via API

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Via APIhttp $LIBRARY \ source_code="(define (addition a b) (+ a b))" | jq ".resource"

"library/573a97f5b95b3941f6000004"

http $SCRIPT \ imports:='["library/573a97f5b95b3941f6000004"]' \ source_code="(addition x 2)" \ inputs:='[{"name": "x", "type": "number"}]' | jq ".resource"

"script/573a9862b95b3941ff000015"

http $EXECUTION \ script=script/573a9862b95b3941ff000015 \ inputs:='[["x", 5]]' | jq ".resource"

"execution/573a987ab95b3941f000000d"

http http://bigml.io/execution/573a987ab95b3941f000000d?$BIGML_AUTH \ | jq ".execution.result"

7

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Via Bindingshttps://gist.github.com/whizzmler/8a849c282a770ac79a1441df5c5ccf62

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Gallery Scripts

UPDATE ME!!!

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Importing from GitHub

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WhizzML in GitHubNEW

https://github.com/whizzml/examples

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WhizzML UI Resources

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Script Editor

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WhizzML REPLNEW

https://bigml.com/whizzml

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Reify

•"Reifies" a resource into a WhizzML script. •Rapid prototyping meets automation. •Coming soon…

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Secret Link Scripts

https://bigml.com/shared/script/oazVtg8t2V2JHFf6PLmenUJbNU

https://bigml.com/dashboard/script/573d53a628eb3e026f000012

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A Gallery of Scriptshttps://bigml.com/gallery/scripts

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Share or Sell•Each script is reviewed internally by BigML Team members.

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Personalizing 1-Click Menus

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Demo

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API Documentation

• https://bigml.com/developers/libraries • https://bigml.com/developers/scripts • https://bigml.com/developers/executions

NEW

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WhizzML PageNEW

https://bigml.com/whizzml

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Documentation

Getting Started withWhizzML

The BigML Team

Version 1.0

MACHINE LEARNING MADE BEAUTIFULLY SIMPLE

Copyright © 2016, BigML, Inc.

WhizzML ReferenceManualThe BigML Team

Version draft

MACHINE LEARNING MADE BEAUTIFULLY SIMPLE

Copyright © 2016, BigML, Inc.

WhizzML TutorialsThe BigML Team

Version draft

MACHINE LEARNING MADE BEAUTIFULLY SIMPLE

Copyright © 2016, BigML, Inc.

NEW

https://bigml.com/whizzml#documentation

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TrainingNEW

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https://bigml.com/events

FREE TrainingNEW

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Spring 2016 Releasehttps://bigml.com/releases

NEW

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Conclusion•Automation is critical to fulfilling the promise of ML •WhizzML can create workflows that:

•Automate repetitive tasks. •Automate model tuning and feature selection.

•Combine ML models into more powerful algorithms.

•Create shareable and re-usable executions.

Questions?

twitter: @whizzmlmail: [email protected]