A new data science approach with cognitive assistance in a data science ecosystem to improve model predictivity.

1. BUILDING BETTER PREDICTIVE MODELS WITH
COGNITIVE ASSISTANCE IN A DATA SCIENCE
ECOSYSTEM
Dr. Alex Liu
Chief Data Scientist
Analytics Services @ IBM
aliu@us.ibm.com
Sep 12, 2018
NASA JPL SVCP

2. ALEX LIU INTRODUCTION
Chief Data Scientist – Analytics Services at
IBM
A Data Scientist Thought Leader
Chief Data Scientist for a few corporations
before joined IBM
Taught advanced data analytics for the
University of South California and the
University of California at Irvine
Consulted for the United Nations, Ingram
Micro …
M.S. and Ph.D. from Stanford University

3. DATA SCIENCE: TURNING DATA INTO VALUE WITH MODELS
Data Science produces insights/values via
a complicated proccese
a big set of tools
3
BigInsights
(HDFS)
Cloudant
(DBaaS)
dashDB
(Analytics)
Swift
(Object
Storage)
SQDB
(Managed
DB2)

4. DATA SCIENCE PROJECTS RETURN VERY
VALUABLE RESULTS
BUT A LOT FAILED
Netflix, for example, integrates data science into each part of their
business; they estimate a billion dollars in incremental value from their
personalization and recommendation alone.
Knight Capital Group, for instance, lost $440 million in 45 minutes after
a mistake in updating a model.
Gartner estimated that 60% of big data projects fail in 2016, and in
2017.
Reproducibility crisis & fast insight demands

5. DATA SCIENCE – COMPLICATED
VERY COMPLICATED FLOWS JUST FOR MODEL BUILDING STAGE
5
• More than 50 different algorithms: SVM, Neural Net, Decision Trees/Forests, Naïve Bayes,
Regression, SMO, k-nearest Neighbor, Clustering, Rules, …
• Combinatorially explosive number of parameter choices per algorithm: kernel type, pruning
strategy, number of trees in a forest, learning rate, …
• Wide variation in performance across different algorithm implementations (e.g., SPSS vs
Python vs WEKA vs SPARK …)
• User-Defined algorithms
• Substantial cost in user and compute time
• User spends time on trying new combinations and parameters
• Computational cost for training a single SVM can exceed 24h
• Selection commonly based on data scientist bias
• Each additional pipeline stage increases complexity dramatically!

6. IMPORTANCE OF AUTOMATIONS & COMMUNITIES
AUTOMATION ~ Compare Data Scientist with and without computer-based
augmentation
Show that computer-augmented data science can reduce time-to-result by an order of
magnitude and improve quality of results
COMMUNITY ~ Self-learn and validate using open competitions or
evaluations (e.g., Kaggle, OpenML), IBM customer engagements
6

7. DS ASSISTED BY AI WITHIN A DS COMMUNITY
1) Bring automation into key areas of large-scale data analysis tasks
Overcome “analytic decision overload” for Data Scientists
Enable Data Scientist to:
view and interact with decision making process in an online fashion
obtain rapid insights from data to answer key questions
2) Integrated System of tools, working with DS communities
An integrated system for scientists to easily handle data and analytical and
application needs
Upload and prepare data from various sources
Cross-platform modeling and machine learning implementation
Cross-platform analytic deployments on Big Data platforms
IBM Research
7
Augumention
Vs.
Automation

8. Db2 Warehouse
on Cloud
IBM
Cloudant
RStudio
Jupyter Notebooks
Data Science
Tools
IBM
Cloud Object
Storage
IBM
Compose
IBM Cloud
Data from the IBM Cloud
& third party clouds
On-premises data
Watson Studio
Watson Data
Platform
Persistence
Cloud Services
WATSON STUDIO LOCAL
IBM Analytics
Engine
Data Steward
Data scientist
Spark ML
Hadoop
Data Refinery
Data Engineer
Cognos
Watson Analytics
Dashboards
Developer
One Platform for
IBM Analytics
Team
32 Different
Connections
Plugin

9. IBM Confidential9

10. IBM Data
Science
Experience
summary

11. IBM Data Science Experience summary

12. TAKING A DATA SCIENCE ECOSYSTEM APPROACH
A DATA SCIENCE ECOSYSTEM HAS THREE BASIC ELEMENTS
1) DATA PORTAL
2) DATA SCIENCE COMMUNITY
3) DATA SCIENCE PLATFORM

13. RMDS COMMUNITIES AT IBM GLENDALE
Pasadena/Glendale Meetup Community
Local face to face community – more than 1100 members
https://www.meetup.com/RMDS_LA/
https://www.linkedin.com/groups/1895501 has 29K participants
Aim to create an environment for utilizing big data analytics to create smart cities and smart commerce

14. 105,000+
collections
349 citizen apps
500,000 data
resources
175 agencies
450 APIs
14
Source: City of LA Mayor’s Tech Advisor Presentation
at RMDS Meetup.
EX1: citizen data science
ecosystem with open data

15. EXAMPLE – 1KM VISIBLE (GOES-R WILL BE
EVEN BETTER)
http://www.ibm.com/weather

16. EX2: A data science ecosystem with weather data
101
010
101
Platform
~ IBM DSX
Weather Data Transaction
Analytical
Insights for
Transformation
Connecting all
the data
scientists from
a DS
community
Applications
Optimizing Operations Solutions
IoT Data

17. A MAJORITY OF RETAIL AND CP EXECUTIVES
INDICATE WEATHER HAS A SIGNIFICANT
IMPACT ON BUSINESS DECISION-MAKING
50%
50%
45%
41%
40%
39%
35%
33%
Work safely
Inventory pricing
Customer interactions
Marketing / messaging
Inventory placements
Routes and transportation
Supply chain / sourcing
Product development
Weather either influences all human decisions or triggers automated actions in the following areas
51%Worker allocation and staff scheduling
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