Rakuten Technology Conference 2019

1. Nov 9, 2019
Sekhar Kalle
Data Science & AI Department
Rakuten, Inc.

2. 2
• Time consuming to
setup environment
• Difficult to access and
understand data
• Need to share data
science knowledge
• Centralized analyst environment
• No machine learning knowledge
• Difficult to get knowledge from
tech unit about data science
Data Scientist/Data Engineer Marketer/ Business Analyst
2
Personas and Key Pain Points in Data Science Ecosystem
• Time consuming to
setup environment
• Difficult to access and
understand data
• Need to share data
science knowledge
• Centralized analyst environment
• No machine learning knowledge
• Difficult to get knowledge from
tech unit about data science
Data Scientist/Data Engineer Marketer/ Business Analyst

3. 3
• Increase Adoption by lowering barrier for Data Scientists to manage the ML pipeline
• Quicker solutioning/turnaround for training/deployment cycles
• Speed of data sciences team with faster business outcomes from data
Knowledge sharing Scalable compute AI for business

4. 4
Preparation
54%
Understand
27%
Only 20% of their time spent
on data science work
Build AI / Data
Model
Environment
Preparation
Understand
Evaluate
We can reduce 90% of the
preparation time

5. 5

6. 6
ML Workbench
Deployment Engine
Knowledge
Community
Data
Preparation
Data
Discovery
Training Evaluation Versioning Deployment Monitoring

7. 7

8. 8
Training
Deployment
Notebooks/Models
Workbench
Platform Runtime
Environment
Monitoring, Alerting
and Tools for high SLA
service management
On-prem Platform
Infrastructure
(K8s)
Infrastructure
(Apache Mesos, GPU)
Platform Runtime
Environment
Monitoring, Alerting
and Tools for high SLA
service management
On-cloud Platform
Infrastructure
(GCP VM, GPU, TPU)
Data Store
(SPDB, Hadoop, etc.)
Replicated Data
Interface
Interface
Data Science Console
Job/
API
Existing
app
Existing
app
Job/
API

9. 9

10. 10

11. 11
CustomerDNA
Customer Predictive
Intelligence
Geo
70+ data scientists use GDSP on daily basis for their machine learning workflows
Using GDSP GEO has developed and hosted model API for logistics last mile solution and RMN coverage map service
CustomerDNA generated models for features prediction and automated the
workflow
Using GDSP
Applications on GDSP
CPI is building models for predicting next purchase propensity and devising
Marketer action across the customer funnel
Brand & Marketing
Group
EC Logistics DSCD + DSAID
Success Story
Data Science Consulting Department is using GDSP for generating insights for many external
clients