Deploying your Predictive Models as a Service via Domino
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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.
Deploying your Predictive Models as a Service via Domino
- 1. Deploying your Predictive Models as a Service via Domino API Endpoint Jo-fai (Joe) Chow Data Scientist at Domino Data Lab 6/16/2015 1LondonR
- 2. Agenda • Background • My Domino Experience o Why o How • Examples (Iris & Stock Market) • Conclusions • Q & A 6/16/2015LondonR 2
- 3. I LondonR! 6/16/2015LondonR 3 All about my PhD project very interesting stuff …
- 5. Recap: what I really do 6/16/2015LondonR 5
- 6. Recap: what I really do 6/16/2015LondonR 6 Since last talk … xgboost(…), h2o.deeplearning(…)
- 7. About Domino Data Lab 6/16/2015LondonR 7
- 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. 6/16/2015LondonR 8
- 9. How I use Domino • Interface o Web or R • Examples o Hello, World! (Iris) o Stock Market Forecast • Code Sharing • Try it Yourself 6/16/2015LondonR 9
- 10. Web Interface 6/16/2015LondonR 10 Control Panel A List of Runs Console
- 11. Web Interface 6/16/2015LondonR 11 Resource Usage (I found it very useful!)
- 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 6/16/2015LondonR 14
- 15. Predictive Model? 6/16/2015LondonR 15 y = f( X1, X2, X3, X4)
- 16. Upload and Run 6/16/2015https://app.dominoup.com/jofaichow/example_iris 16 Upload the R script to Domino (Web / R) Start the Run (Web / R)
- 17. Evaluate and Save 6/16/2015LondonR 17 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 6/16/2015LondonR 18 Model This script 1) loads the model, 2) takes four numeric inputs (X1, X2, X3 & X4) and then 3) returns a prediction.
- 19. Deploy 6/16/2015LondonR 19 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? 6/16/2015LondonR 20
- 21. Python API Example 6/16/2015LondonR 21 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 6/16/2015LondonR 22
- 23. Predictive Model 6/16/2015LondonR 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 app.dominoup.com/jofaichow/example_stock
- 24. Scheduled Runs 6/16/2015LondonR 24 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
- 26. Automatic Version Control 6/16/2015LondonR 26 Latest Version One of the Previous Versions (I was experimenting with ggplot2)
- 27. Stock API Endpoint 6/16/2015LondonR 27 Stock Symbol (Ticker) for Query
- 28. Code Sharing 6/16/2015LondonR 28 Control Panel Settings One Click
- 29. Try it Yourself 6/16/2015LondonR 29 Register at www.dominodatalab.com Help, Quick Start, Forum at support.dominodatalab.com
- 30. Try it Yourself 6/16/2015LondonR 30 Go to https://app.dominoup.com/jofaichow/example_iris
- 31. Set up your first API Endpoint in Minutes 6/16/2015LondonR 31 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. 6/16/2015LondonR 32
- 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 6/16/2015LondonR 33