API, WhizzML and Apps

BigML, Inc 1
Automation
Poul Petersen @pejpgrep
CIO, BigML, Inc @bigmlcom
API, WhizzML and Predictive Applications

BigML, Inc 2ML Crash Course - API/WhizzML/Predictive Apps
BigML Architecture
Tools
REST API
Distributed Machine Learning Backend
Source
Server
Dataset
Server
Model
Server
Prediction
Server
Sample
Server
WhizzML
Server
Evaluation
Server
Web-based Frontend
Visualizations
Smart Infrastructure
(auto-deployable, auto-scalable)

The Need for a ML API
• Workﬂow Automation - reduce drudgery
• Abstraction - reuse code
• Composability - powerful combinations of APIs
• Integration - Dashboard or UI component
• Automate deployment
• Repeatable results

Predictive Applications
Collect
& Format
Data
Deﬁne
ML
Problem
ETL
Model &
Evaluate
no
yes
Explore
Collect
& Format
Data
Model
Automate
Consume
& Monitor
Predict
Score
Label
Drift &
Anomaly
feature

engineer
Not

Possible
tune

algorithm
Goal
Met?

BigML API Endpoint
https://bigml.io/ / /{id}?{auth}
source
dataset
model
ensemble
prediction
batchprediction
evaluation
…
andromeda
dev
dev/andromeda
• Path elements:
• /andromeda specifies the API version (optional)
• /dev specifies development mode
• if not specified, then latest API in production mode
• {id} is required for PUT and DELETE
• {auth} contains url parameters username and api_key
• api_key can be an alternative key

BigML API Endpoint
https://bigml.io/...{JSON} {JSON}
Operation HTTP Method Semantics
CREATE POST
Creates a new resource. Returns a JSON document
including a unique identifier.
RETRIEVE GET
Retrieves either a specific resource or a list of
resources.
UPDATE PUT Updates a resource. Only certain fields are putable.
DELETE DELETE Deletes a resource

BigML Bindings
https://github.com/bigmlcom/io

Python Binding Overview
Operation HTTP Method Binding Method
CREATE POST api.create_<resource>(from, {opts})
RETRIEVE GET
api.get_<resource>(id, {opts})
api.list_<resource>({opts})
UPDATE PUT api.update_<resource>(id, {opts})
DELETE DELETE api.delete_<resource>(id)
• Where <resource> is one of: source, dataset, model, ensemble, evaluation, etc
• id is a resource identiﬁer or resource dict
• from is a resource identiﬁer, dict, or string depending on context

Diabetes Anomalies
DIABETES
SOURCE
DIABETES
DATASET
TRAIN SET
TEST SET
ALL
MODEL
CLEAN
DATASET
FILTER
ALL
MODEL
ALL
EVALUATION
CLEAN
EVALUATION
COMPARE
EVALUATIONS
ANAOMALY
DETECTOR

WhizzML
• Complete programming language
• Machine Learning operations are first-class citizens
• Server-side execution abstracts infrastructure
• API First! - Everything is composable
• Shareable
A Domain-Specific Language (DSL) for
automating Machine Learning workflows.

WhizzML vs API
WhizzML API
/
Bindings
Executes
server-‐side

Zero
latency

Paralleliza?on
built-‐in

Sharing
built-‐in

Code
agnos?c
workflows

Workflows
can
be
UI
integrated

Requires
local
execu?on

Every
API
call
has
latency

Manual
paralleliza?on

Manual
sharing

Code
specific
workflows

Workflows
external
to
UI

WhizzML vs Flatline
WhizzML Flatline
Concerned
with
resources

Turing
complete

Op?mized
for
paralleliza?on
Concerned
with
datasets

More
speciﬁc
to
features

Op?mized
for
speed

Simple Workﬂow
SOURCE DATASET MODEL

Redﬁn Workﬂow
Model
Predicts
Sale Price
Sold
Homes
Compare
List to
Prediction

Redﬁn Workﬂow
MODEL
FILTERSOLD HOMES
BATCH
PREDICTION
NEW FEATURES
DATASET DEALS
DATASET
FILTERFORSALE HOMES NEW FEATURES

WhizzML Resources
LIBRARY
CITY 1 SOLD HOMES
CITY 1 DEALS
DATASET
EXECUTION
CITY 1 FORSALE HOMES
SCRIPT

WhizzML Resources
LIBRARY
CITY 2 SOLD HOMES
CITY 2 DEALS
DATASET
EXECUTION
CITY 2 FORSALE HOMES
SCRIPT

Scriptify
• "Reiﬁes" a resource into a WhizzML script.
• Rapid prototyping meets automation.

WhizzML FE
Worth More
Worth Less

WhizzML FE
LATITUDE LONGITUDE REFERENCE
LATITUDE
REFERENCE
LONGITUDE
44.583 -123.296775 44.5638 -123.2794
44.604414 -123.296129 44.5638 -123.2794
44.600108 -123.29707 44.5638 -123.2794
44.603077 -123.295004 44.5638 -123.2794
44.589587 -123.301154 44.5638 -123.2794
Distance (m)
700
30.4
19.38
37.8
23.39
Flatline!

WhizzML FE
https://en.wikipedia.org/wiki/Haversine_formula

WhizML FE
LIBRARY
SCRIPT
Haversine

WhizzML FE
Fix Missing Values in a “Meaningful” Way
Filter Zeros
Model  
insulin
Predict  
insulin
Select  
insulin
Fixed 
Dataset
Amended 
Dataset
Original 
Dataset
Clean 
Dataset

WhizzML Workflow Types
Op?miza?on
Model
or
Ensemble

Best-‐First
Features

SMACdown
Algorithms
Stacked
Generaliza?on

Gradient
boos?ng

Cross
Valida?on

Transforma?ons
Flatline
Wrappers

Remove
Anomalies
Domain
Specific
Applica?on
Workflow

Repe??ve
Tasks

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}

Model Selection
ENSEMBLE LOGISTIC
REGRESSION
EVALUATION
SOURCE DATASET
TRAINING
TEST
MODEL
EVALUATIONEVALUATION
CHOOSE

Model Tuning
ENSEMBLE
N=20
EVALUATION
SOURCE DATASET
TRAINING
TEST
EVALUATIONEVALUATION
ENSEMBLE
N=10
ENSEMBLE
N=1000
CHOOSE

SMACdown
• How many models?
• How many nodes?
• Missing splits or not?
• Number of random candidates?
• Balance the objective?
SMACdown can tell you!

Path to Automatic ML
time
Automation
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

Higher Level Algorithms
• Stacked Generalization
• Boosting
• Adaboost
• Logitboost
• Martingale Boosting
• Gradient Boosting

Stacked Generalization
ENSEMBLE
LOGISTIC
REGRESSION
SOURCE DATASET
MODEL
BATCH
PREDICTION
BATCH
PREDICTION
BATCH
PREDICTION
EXTENDED
DATASET
EXTENDED
DATASET
EXTENDED
DATASET
LOGISTIC
REGRESSION

Why WhizzML
• 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.

API, WhizzML and Apps

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