The document discusses serverless data science and how deploying data science models as REST API endpoints can be time consuming and costly. It introduces QBit as a serverless platform for deploying R packages and functions as REST APIs to address the deployment bottleneck in data science workflows. Examples are provided of using QBit to deploy an R portfolio optimization strategy as a REST endpoint to be used within a web application.

2. Serverless Data Science
Mario Annau
Quantargo
DSC 5.0
November 20, 2019
Belgrade

3. Curiosity
● The “newest” hype in cloud-land
● Low maintenance
● Easy Scalability
● Cheap
Pain
● Data Science Models are typically deployed as REST
API endpoints to production.
● Workflow based on Docker containers is time
consuming to setup and requires devops teams
● Increasing number of models including maintenance
is costly.

4. Deployment is HARD
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SpaceX rocket and Israeli satellite destroyed in launch pad explosion

5. The Data Science Workflow
Raw Data
Dataset
Transform
Ingest
Model
Fit
API
Deploy

6. The Deployment Bottleneck
Raw Data
Dataset
Transform
Ingest
Model
Fit
API
Deploy
Re-Fit
Fix Data

7. Pro
● Fast to deploy
● Pricing per Invocation
● Automatic scaling
Con
250 MB
● R runtime needs to be created
● Package compilation

8. To make it happen, we
● Shrink runtime and package
dependencies
● Maintain package repository,
currently ~14000 (95%) CRAN
R packages.
● Create usable interfaces from
the Console and Web
~10 secs for updates
function.R
objects
packagesruntime

9. Ausblick
www.quantargo.com/qbit-functions

10. Example
● We have developed a portfolio optimization strategy
which returns the optimal weights based on current
market returns.
● We want to deploy the strategy as a REST endpoint so
that a Web-Developer can use it within some App.
● We use R

11. library(PortfolioAnalytics)
library(ROI)
library(ROI.plugin.symphony)
sym <- function(R) {
funds <- colnames(R)
#' Set up initial portfolio with basic constraints.
init.portf <- portfolio.spec(assets=funds)
init.portf <- add.constraint(portfolio=init.portf, type="full_investment")
init.portf <- add.constraint(portfolio=init.portf, type="long_only")
#' Add objectives.
mines.portf <- add.objective(portfolio=init.portf, type="risk", name="ES")
opt.mines.symphony <- optimize.portfolio(R, mines.portf, optimize_method="symphony")
opt.mines.symphony
}
From PortfolioAnalytics package demo/demo_roi_solvers.R

12. QBit Roadmap
● Release R-package for deployment
● Include more runtimes (Python, Julia)
● Document generation
● Scheduling
● More Show cases in the areas of Machine Learning, Web scraping,
Optimization, ...
Release Trial for R
Add Features, Show cases
Additional
runtimes
Q4 2019 Q1 2020

13. Your Turn
1. Register and create API key
quantargo.com/signup
1. Test functions through the web interface
quantargo.com/qbit-functions
1. We inform you when the R-package is ready