The document discusses how Domino Data Lab can be used to deploy predictive models as APIs. It provides examples of using Domino to build, evaluate, and deploy predictive models for the Iris dataset and stock market forecasting. Key features discussed include the web and R interfaces, code sharing, scheduled runs, automatic version control, and publishing models as APIs for other applications to access.

1. Deploying your Predictive
Models as a Service via
Domino API Endpoint
Jo-fai (Joe) Chow
Data Scientist at Domino Data Lab
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2. Agenda
• Background
• My Domino Experience
o Why
o How
• Examples (Iris & Stock Market)
• Conclusions
• Q & A
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3. I LondonR!
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All about my PhD project
very interesting stuff …

4. First Collaboration
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http://blog.dominoup.com/using-r-h2o-and-domino-for-a-kaggle-competition/

5. Recap: what I really do
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6. Recap: what I really do
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Since last talk …
xgboost(…),
h2o.deeplearning(…)

7. About Domino Data Lab
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8. Why I use Domino
• 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.
• I do not have time/skills for every single task.
• I can use Domino to fill the gaps.
• Focus on understanding problems, improving
models and presenting results.
• Speed up analysis in just a few clicks.
• More time for family and other stuff.
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9. How I use Domino
• Interface
o Web or R
• Examples
o Hello, World! (Iris)
o Stock Market Forecast
• Code Sharing
• Try it Yourself
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10. Web Interface
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Control Panel
A List of Runs
Console

11. Web Interface
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Resource Usage (I found it very useful!)

12. R Interface
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13. R Interface
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14. “Hello, World!” Example
• Classic dataset - Iris
• Four numeric features / predictors (x)
o Sepal Length, Sepal Width, Petal Length and Petal Width
• One categorical target (y)
o Three species of Iris – Setosa, Versicolor and Virginica
• Using R to build a simple predictive model
• Saving the model for future use
• Deploying the model as web service
• Automatic version control
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15. Predictive Model?
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y = f( X1, X2, X3, X4)

16. Upload and Run
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Upload the R script to
Domino (Web / R)
Start the Run
(Web / R)

17. Evaluate and Save
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Print “Random Forest”
model summary
Model with highest
10-fold cross-
validation accuracy
(i.e. best parameter
setting)
Include statistics for
future comparison
Finally, save the model
for future use

18. Deploy
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Model
This script 1) loads the model, 2) takes four numeric inputs
(X1, X2, X3 & X4) and then 3) returns a prediction.

19. Deploy
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Point to that script
Specify the function to call
Publish or unpublish the API
Domino automatically keeps all versions of your API

20. How to use the API?
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21. Python API Example
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X1, X2, X3 and X4
The four Iris features:
Sepal Length, Sepal Width,
Petal Length and Petal Width

22. Stock Market Forecast
• Historical stock data from Yahoo!
• Using R to generate numeric features (x)
• Target (y) – Next Trading Day % Change in Closing
Price
• Using R to build ensembles for forecast
• Configure scheduled runs
• Automatic version control
• API
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23. Predictive Model
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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
app.dominoup.com/jofaichow/example_stock

24. Scheduled Runs
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Point to the R script
Schedule to run at a certain time every
Weekday (more options available)
Re-publish API endpoint so it uses the latest results
Select different hardware tiers
Notify your friends / colleagues / clients

25. Results Notification
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Summary PDF

26. Automatic Version
Control
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Latest Version One of the Previous Versions
(I was experimenting with ggplot2)

27. Stock API Endpoint
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Stock Symbol (Ticker) for Query

28. Code Sharing
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Control Panel  Settings  One Click

29. Try it Yourself
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Register at www.dominodatalab.com
Help, Quick Start, Forum at support.dominodatalab.com

30. Try it Yourself
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Go to https://app.dominoup.com/jofaichow/example_iris

31. Set up your first API
Endpoint in Minutes
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Point it to your own project
Insert your own API key

32. Conclusions
• Data science is complicated.
• Our time is important.
• I can use Domino to save time.
• It helps me to tackle some challenges
that are outside my comfort zone.
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33. Thanks!
• Mango Solutions
• My Colleagues at Domino
• More Info and Feedback
o jofai@dominoup.com
o Twitter: @matlabulous
o http://blog.dominodatalab.com/
• Code
o Iris Example –
https://app.dominoup.com/jofaichow/example_iris
o Stock Example –
https://app.dominoup.com/jofaichow/example_stock
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