The document discusses BigML, an API and platform for machine learning. It provides an overview of BigML's architecture with layers for the API, frontend, backend, and other services. It also summarizes the API including common operations like create, retrieve, update and delete resources. Examples are given of building predictive applications with the BigML API including processing user agent logs to predict color preferences and making predictions from JavaScript. Bindings for different programming languages are highlighted as well as BigMLer, a command line tool for machine learning workflows.

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API

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Today’s Webinar
• Speaker:
• Poul Petersen, CIO
• Moderator:
• Andrew Shikiar, VP Business Development
• Enter questions into chat box – we’ll answer some
via text; others at the end of the session
• For direct follow-up, email us at info@bigml.com

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BigML Architecture
sky
wintermute
apian
https://bigml.com
https://bigml.io API Layer
Frontend
Visualization Layer
Backend
Computation Layer
Other Services

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API Bindings Overview
!4
API Introduction / Demo with1
Predictive Application Demo2
3
Programmatic ML Examples with4
Agenda
BigMLer - a command line tool for ML5

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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

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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

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Predict Color Pref?

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App Architecture
Web
Server
BrowserLogs
Batch Upload / Model Real-Time
request
predict
custom
experience

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Log Data
user_agent color
Mozilla/5.0 (Windows NT 6.1; WOW64; rv Yellow
Mozilla/5.0 (X11; Linux i686) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/
33.0.1750.146 Safari/537.36
Green
Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko)
Chrome/32.0.1700.107 Safari/537.36
Green
Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko)
Chrome/32.0.1700.107 Safari/537.36
Yellow
Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/
33.0.1750.146 Safari/537.36
Red
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like
Gecko) Chrome/32.0.1700.107 Safari/537.36
Red
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like
Gecko) Chrome/33.0.1750.117 Safari/537.36
Yellow
Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/
33.0.1750.117 Safari/537.36
Yellow
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like
Gecko) Chrome/33.0.1750.146 Safari/537.36
Red

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New Features
Mozilla/5.0 (iPhone; CPU iPhone OS 7_0_6 like Mac OS X) AppleWebKit/537.51.1 (KHTML, like Gecko) Version/7.0 Mobile/
11B651 Safari/9537.5
Mobile Safari
browser browser version os os version device
7 iOS 7.0.6 iPhone
User-agent
parser

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New Features
browser version os os version device color
Other Windows 7 Other Yellow
Chrome 33.0.1750 Linux Other Green
Chrome 32.0.1700 Windows 8 Other Green
Chrome 32.0.1700 Windows 7 Other Yellow
Chrome 33.0.1750 Windows XP Other Red
Chrome 32.0.1700 Mac OS X 10.9.1 Other Red
Chrome 33.0.1750 Mac OS X 10.9.1 Other Yellow
Chrome 33.0.1750 Windows 7 Other Yellow
Chrome 33.0.1750 Mac OS X 10.9.1 Other Red

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Model

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JS Predictions
. . .

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Predictions

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Gist
http://bl.ocks.org/osroca/9474489

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BigML Bindings!
https://bigml.com/developers
...And more:

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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)
Binding Overview
• 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

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ToyBoost*
orig
dataset
dataset
+weight
model
source
+predict
batch
predict
dataset
+predict
*For Python Bindings Demonstration

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BigMLer
•BigMLer wraps BigML’s API Python bindings
•Issue complete train/evaluation cycle in one command
•Can do cross-validation
•Remote/Local predictions or even PredictServer
•Define field types in a flat file
•Multi-Label classifications
BigMLer makes BigML even easier!