Date post: | 20-Feb-2017 |
Category: |
Data & Analytics |
Upload: | domino-data-lab |
View: | 2,090 times |
Download: | 1 times |
LondonR
Deploying your Predictive Models as a Service via Domino API Endpoints
Jo-fai (Joe) Chow Data Scientist at Domino Data Lab
6152015 1
LondonR
Agendabull Background bull My Domino Experience
o Why
o How
bull Examples (Iris amp Stock Market)
bull Conclusions bull Q amp A
6152015 2
LondonR
I LondonR
6152015 3
All about my PhD project very interesting stuff hellip
LondonR
First Collaboration
6152015 4
httpblogdominoupcomusing-shy‐‑r-shy‐‑h2o-shy‐‑and-shy‐‑domino-shy‐‑for-shy‐‑a-shy‐‑kaggle-shy‐‑competition
LondonR
Recap what I really do
6152015 5
LondonR
Recap what I really do
6152015 6
Since last talk hellip
xgboost(hellip) h2odeeplearning(hellip)
LondonR
About Domino Data Lab
6152015 7
LondonR
Why I use Dominobull Data science is complicated
o Knowing how to fit a model is not enough o Variety of challenges from data analysis to production o There is no one-size-fits-all solution
bull I do not have timeskills for every single task bull I can use Domino to fill the gaps bull Focus on understanding problems improving models
and presenting results bull Speed up analysis in just a few clicks bull More time for family and other stuff
6152015 8
LondonR
How I use Dominobull Interface
o Web or R
bull Examples o Hello World (Iris)
o Stock Market Forecast
bull Code Sharing bull Try it Yourself
6152015 9
LondonR
Web Interface
6152015 10
Control Panel
A List of Runs
Console
LondonR
Web Interface
6152015 11
Resource Usage (I found it very useful)
LondonR
R Interface
6152015 12
LondonR
R Interface
6152015 13
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Agendabull Background bull My Domino Experience
o Why
o How
bull Examples (Iris amp Stock Market)
bull Conclusions bull Q amp A
6152015 2
LondonR
I LondonR
6152015 3
All about my PhD project very interesting stuff hellip
LondonR
First Collaboration
6152015 4
httpblogdominoupcomusing-shy‐‑r-shy‐‑h2o-shy‐‑and-shy‐‑domino-shy‐‑for-shy‐‑a-shy‐‑kaggle-shy‐‑competition
LondonR
Recap what I really do
6152015 5
LondonR
Recap what I really do
6152015 6
Since last talk hellip
xgboost(hellip) h2odeeplearning(hellip)
LondonR
About Domino Data Lab
6152015 7
LondonR
Why I use Dominobull Data science is complicated
o Knowing how to fit a model is not enough o Variety of challenges from data analysis to production o There is no one-size-fits-all solution
bull I do not have timeskills for every single task bull I can use Domino to fill the gaps bull Focus on understanding problems improving models
and presenting results bull Speed up analysis in just a few clicks bull More time for family and other stuff
6152015 8
LondonR
How I use Dominobull Interface
o Web or R
bull Examples o Hello World (Iris)
o Stock Market Forecast
bull Code Sharing bull Try it Yourself
6152015 9
LondonR
Web Interface
6152015 10
Control Panel
A List of Runs
Console
LondonR
Web Interface
6152015 11
Resource Usage (I found it very useful)
LondonR
R Interface
6152015 12
LondonR
R Interface
6152015 13
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
I LondonR
6152015 3
All about my PhD project very interesting stuff hellip
LondonR
First Collaboration
6152015 4
httpblogdominoupcomusing-shy‐‑r-shy‐‑h2o-shy‐‑and-shy‐‑domino-shy‐‑for-shy‐‑a-shy‐‑kaggle-shy‐‑competition
LondonR
Recap what I really do
6152015 5
LondonR
Recap what I really do
6152015 6
Since last talk hellip
xgboost(hellip) h2odeeplearning(hellip)
LondonR
About Domino Data Lab
6152015 7
LondonR
Why I use Dominobull Data science is complicated
o Knowing how to fit a model is not enough o Variety of challenges from data analysis to production o There is no one-size-fits-all solution
bull I do not have timeskills for every single task bull I can use Domino to fill the gaps bull Focus on understanding problems improving models
and presenting results bull Speed up analysis in just a few clicks bull More time for family and other stuff
6152015 8
LondonR
How I use Dominobull Interface
o Web or R
bull Examples o Hello World (Iris)
o Stock Market Forecast
bull Code Sharing bull Try it Yourself
6152015 9
LondonR
Web Interface
6152015 10
Control Panel
A List of Runs
Console
LondonR
Web Interface
6152015 11
Resource Usage (I found it very useful)
LondonR
R Interface
6152015 12
LondonR
R Interface
6152015 13
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
First Collaboration
6152015 4
httpblogdominoupcomusing-shy‐‑r-shy‐‑h2o-shy‐‑and-shy‐‑domino-shy‐‑for-shy‐‑a-shy‐‑kaggle-shy‐‑competition
LondonR
Recap what I really do
6152015 5
LondonR
Recap what I really do
6152015 6
Since last talk hellip
xgboost(hellip) h2odeeplearning(hellip)
LondonR
About Domino Data Lab
6152015 7
LondonR
Why I use Dominobull Data science is complicated
o Knowing how to fit a model is not enough o Variety of challenges from data analysis to production o There is no one-size-fits-all solution
bull I do not have timeskills for every single task bull I can use Domino to fill the gaps bull Focus on understanding problems improving models
and presenting results bull Speed up analysis in just a few clicks bull More time for family and other stuff
6152015 8
LondonR
How I use Dominobull Interface
o Web or R
bull Examples o Hello World (Iris)
o Stock Market Forecast
bull Code Sharing bull Try it Yourself
6152015 9
LondonR
Web Interface
6152015 10
Control Panel
A List of Runs
Console
LondonR
Web Interface
6152015 11
Resource Usage (I found it very useful)
LondonR
R Interface
6152015 12
LondonR
R Interface
6152015 13
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Recap what I really do
6152015 5
LondonR
Recap what I really do
6152015 6
Since last talk hellip
xgboost(hellip) h2odeeplearning(hellip)
LondonR
About Domino Data Lab
6152015 7
LondonR
Why I use Dominobull Data science is complicated
o Knowing how to fit a model is not enough o Variety of challenges from data analysis to production o There is no one-size-fits-all solution
bull I do not have timeskills for every single task bull I can use Domino to fill the gaps bull Focus on understanding problems improving models
and presenting results bull Speed up analysis in just a few clicks bull More time for family and other stuff
6152015 8
LondonR
How I use Dominobull Interface
o Web or R
bull Examples o Hello World (Iris)
o Stock Market Forecast
bull Code Sharing bull Try it Yourself
6152015 9
LondonR
Web Interface
6152015 10
Control Panel
A List of Runs
Console
LondonR
Web Interface
6152015 11
Resource Usage (I found it very useful)
LondonR
R Interface
6152015 12
LondonR
R Interface
6152015 13
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Recap what I really do
6152015 6
Since last talk hellip
xgboost(hellip) h2odeeplearning(hellip)
LondonR
About Domino Data Lab
6152015 7
LondonR
Why I use Dominobull Data science is complicated
o Knowing how to fit a model is not enough o Variety of challenges from data analysis to production o There is no one-size-fits-all solution
bull I do not have timeskills for every single task bull I can use Domino to fill the gaps bull Focus on understanding problems improving models
and presenting results bull Speed up analysis in just a few clicks bull More time for family and other stuff
6152015 8
LondonR
How I use Dominobull Interface
o Web or R
bull Examples o Hello World (Iris)
o Stock Market Forecast
bull Code Sharing bull Try it Yourself
6152015 9
LondonR
Web Interface
6152015 10
Control Panel
A List of Runs
Console
LondonR
Web Interface
6152015 11
Resource Usage (I found it very useful)
LondonR
R Interface
6152015 12
LondonR
R Interface
6152015 13
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
About Domino Data Lab
6152015 7
LondonR
Why I use Dominobull Data science is complicated
o Knowing how to fit a model is not enough o Variety of challenges from data analysis to production o There is no one-size-fits-all solution
bull I do not have timeskills for every single task bull I can use Domino to fill the gaps bull Focus on understanding problems improving models
and presenting results bull Speed up analysis in just a few clicks bull More time for family and other stuff
6152015 8
LondonR
How I use Dominobull Interface
o Web or R
bull Examples o Hello World (Iris)
o Stock Market Forecast
bull Code Sharing bull Try it Yourself
6152015 9
LondonR
Web Interface
6152015 10
Control Panel
A List of Runs
Console
LondonR
Web Interface
6152015 11
Resource Usage (I found it very useful)
LondonR
R Interface
6152015 12
LondonR
R Interface
6152015 13
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Why I use Dominobull Data science is complicated
o Knowing how to fit a model is not enough o Variety of challenges from data analysis to production o There is no one-size-fits-all solution
bull I do not have timeskills for every single task bull I can use Domino to fill the gaps bull Focus on understanding problems improving models
and presenting results bull Speed up analysis in just a few clicks bull More time for family and other stuff
6152015 8
LondonR
How I use Dominobull Interface
o Web or R
bull Examples o Hello World (Iris)
o Stock Market Forecast
bull Code Sharing bull Try it Yourself
6152015 9
LondonR
Web Interface
6152015 10
Control Panel
A List of Runs
Console
LondonR
Web Interface
6152015 11
Resource Usage (I found it very useful)
LondonR
R Interface
6152015 12
LondonR
R Interface
6152015 13
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
How I use Dominobull Interface
o Web or R
bull Examples o Hello World (Iris)
o Stock Market Forecast
bull Code Sharing bull Try it Yourself
6152015 9
LondonR
Web Interface
6152015 10
Control Panel
A List of Runs
Console
LondonR
Web Interface
6152015 11
Resource Usage (I found it very useful)
LondonR
R Interface
6152015 12
LondonR
R Interface
6152015 13
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Web Interface
6152015 10
Control Panel
A List of Runs
Console
LondonR
Web Interface
6152015 11
Resource Usage (I found it very useful)
LondonR
R Interface
6152015 12
LondonR
R Interface
6152015 13
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Web Interface
6152015 11
Resource Usage (I found it very useful)
LondonR
R Interface
6152015 12
LondonR
R Interface
6152015 13
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
R Interface
6152015 12
LondonR
R Interface
6152015 13
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
R Interface
6152015 13
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
ldquoHello Worldrdquo Examplebull Classic dataset - Iris bull Four numeric features predictors (x)
o Sepal Length Sepal Width Petal Length and Petal Width
bull One categorical target (y) o Three species of Iris ndash Setosa Versicolor and Virginica
bull Using R to build a simple predictive model bull Saving the model for future use bull Deploying the model as web service bull Automatic version control
6152015 14
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Predictive Model
6152015 15
y = f( X1 X2 X3 X4)
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
httpsappdominoupcomjofaichowexample_iris
Upload and Run
6152015 16
Upload the R script to Domino (Web R)
Start the Run (Web R)
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Evaluate and Save
6152015 17
Print ldquoRandom Forestrdquo model summary
Model with highest 10-shy‐‑fold cross-shy‐‑ validation accuracy (ie best parameter setting)
Include statistics for future comparison
Finally save the model for future use
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Deploy
6152015 18
Model
This script 1) loads the model 2) takes four numeric inputs (X1 X2 X3 amp X4) and then 3) returns a prediction
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Deploy
6152015 19
Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
How to use the API
6152015 20
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Python API Example
6152015 21
X1 X2 X3 and X4
The four Iris features Sepal Length Sepal Width Petal Length and Petal Width
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Stock Market Forecastbull Historical stock data from Yahoo bull Using R to generate numeric features (x) bull Target (y) ndash Next Trading Day Change in Closing
Price bull Using R to build ensembles for forecast bull Configure scheduled runs bull Automatic version control bull API
6152015 22
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Predictive Model
6152015 23
Historical stock price data from Yahoo
x Multiple Technical Analysis Indicators
y Next Day Change in Closing Price
Predictive Model Ensemble of xgboost models For more info see appdominoupcomjofaichowexample_stock
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Scheduled Runs
6152015 24
Point to the R script
Schedule to run at a certain time every Weekday (more options available)
Re-shy‐‑publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends colleagues clients
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Results Notification
6152015 25
Summary PDF
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Automatic Version Control
6152015 26
Latest Version One of the Previous Versions (I was experimenting with ggplot2)
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Stock API Endpoint
6152015 27
Stock Symbol (Ticker) for Query
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Code Sharing
6152015 28
Control Panel agrave Settings agrave One Click
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Try it Yourself
6152015 29
Register at wwwdominodatalabcom Help Quick Start Forum at supportdominodatalabcom
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Try it Yourself
6152015 30
Go to httpsappdominoupcomjofaichowexample_iris
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Set up your first API Endpoint in Minutes
6152015 31
Point it to your own projectInsert your own API key
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Conclusionsbull Data science is complicated bull Our time is important bull I can use Domino to save time bull It helps me to tackle some challenges
that are outside my comfort zone
6152015 32
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33
LondonR
Thanksbull Mango Solutions bull My Colleagues at Domino bull More Info and Feedback
o jofaidominoupcom o Twitter matlabulous
o httpblogdominodatalabcom
bull Code o Iris Example ndash httpsappdominoupcomjofaichowexample_iris o Stock Example ndash httpsappdominoupcomjofaichow
example_stock
6152015 33