This document discusses data science and machine learning concepts and tools. It introduces the IBM Data Science Experience (DSX) and Watson Machine Learning (WML) products, which provide environments for data scientists and developers to build machine learning models. DSX offers notebooks, IDEs and collaboration tools, while WML focuses on visual model creation, access to algorithms, full ML workflows and APIs. It then demonstrates these products.

1. Building and
Innovating with Data
Ritika Gunnar
VP, Data & Analytics
@ritika_gunnar

2. Ingest
data
Transform:
clean
Create
and build
model
Evaluate
Deliver and
deploy
model
Communicate
results
Understand
problem and
domain
Explore and
understand
data
Transform:
shape
OUTPUT
ANALYSIS
INPUT Architects how data is
organized & ensures operability
Gets deep into the data to draw
hidden insights for the business
Works with data to apply insights
to the business strategy
Plugs into data and models &
writes code to build apps
Data Engineer
Data Scientist
Business Analyst
App Developer
Enabling a culture for the data hungry

3. Major Innovation
Analytics
Operating System
Collaborative Experiences
Analytics
IDE
Expanding Community
Analytics
Open Ecosystem
Accelerating the Momentum

4. Built-in learning
to get started or go
the distance
The best of open
source and IBM
value-add to create
state-of-the-art data
products
Community and
social features that
provide meaningful
collaboration
Learn Create Collaborate
Data Science Experience

5. IBM Data Science Experience
Community Open Source IBM Added Value
Powered by IBM Watson Data Platform
- Find tutorials and datasets
- Connect with other data scientist
- Ask questions
- Read articles and papers
- Fork and share projects
- Code in Scala/Python/R/SQL
- Jupyter Notebooks
- RStudio IDE and Shiny apps
- Apache Spark
- Your favorite libraries
- Modeler UI / Statistics
- Prescriptive Analytics
- Auto-data preparation
- Auto-modeling
- Advanced Visualizations
- Model management and deployment
IBM Data Science Experience provides an environment that brings together everything that a data scientist needs today. It includes the most popular Open
Source tools and IBM unique value-add functionalities with community and social features integrated as a first class citizen to make data scientists more
successful.
Be a better Data Scientist

6. What is machine learning?
Wikipedia: subfield of computer science that "gives computers the
ability to learn without being explicitly programmed" (Arthur Samuel,
1959).

7. Drive and Automate Machine Learning as a Service
UI and
Collaboration
Watson
Analytics
Canvas /
Grid
Notebooks
APIs
DevOps
Business
Professional
Data Scientist
App Developer
Drive and
Control
Ingest Train Model
Collaborate Deploy

8. Introducing a new way to do Machine Learning
powered by Watson
1. Easier creation of Models using visual tools:
• GUI model creation through Canvas
• Automated creation using CADS (particularly for Application Developer)
2. Access to wide range of algorithms and execution engines
• Spark ML
• SPSS Modeler
• Scikit-learn
• TensorFlow
3. Full Machine Learning workflow as a service
• Deployment of models to production without requiring coding
4. APIs for developers to train and score Machine Learning models
• Data Scientists access via DSX, Application Developers via. Bluemix WML service
5. Deployment and Model Management
• Deploy into Batch, Streaming and Real-time applications
• Automated model evaluation and update

9. DATA SCIENCE EXPERIENCE
& WATSON MACHINE LEARNING
DEMO
ARMAND RUIZ – OFFERING MANAGER DSX & WATSON ML
@armand_ruiz

10. New approaches to data
Uses of Data
BI & Data
Warehousing
Modernization
Operations
Cost Reduction
Self-Service
Analytics
Insight-Driven
New Business
Models
Transformation
Value

11. A shift in expectations
Self-
service
access
with trust &
security
Remove
silos
created
by systems
& tools
Drive more
intelligence
faster than
ever before
Innovate with
open source
and the
community
“Make Data
Simple”

12. Enabling a culture
for the data hungry
Business
Professional
Data
Scientist
Data
Engineer
App
Developer
Chief Data
Officer

13. Major Innovation
Analytics
Operating System
Collaborative Experiences
Analytics
IDE
Expanding Community
Analytics
Open Ecosystem
Accelerating the Momentum
?

14. The first data and analytics platform
for the cognitive business
IBM Watson Data Platform
Platform. Ecosystem. Method.

15. IBM Watson Data Platform
A new way to experience data
Data
Engineering
Data
Science
Business
Analysis
App
Development
Experiences
task-specific, collaborative
Data and Analytics Services
comprehensive
open ● intelligent ● hybrid

16. Delivering Trusted Insights
Making data simple &
without risk
Accelerating trusted
insights
Delivering data
everywhere
Employing governance strategically
Unified Governance
Leveraging hybrid cloud approach
Business
Professional
Data
Engineer
Data
Scientist
App
Developer

17. Find Share Collaborate
common data, pipelines and projects
analytics operating system
Data Sources
• On-premises / cloud
• Structured / unstructured
• In-motion/ at-rest
• Internal / external
Govern
Hadoop
NoSQL / SQL
Object store
Discovery / Exploration
Machine learning
Models development
Reports / Dashboards
Applications
APIs
Integration
Matching / Quality
Streaming
Persist
Analyze
Ingest Deploy
Data Engineering Data Science Business Analysis App Development
Iterate
IBM Watson Data Platform
A new way to experience data

18. The IBM DataFirst Method provides the strategy, expertise and
game plan to ensure organizations gain the most value from data.
Data Management Track
Efficiency
Data Lake
Track
Modernization
Data Science
Track
Democratization
Data in Action
Track
Monetization
The Method

19. TRUSTED
PARTNERS
COMMUNITY
ACCEPTED
OPEN
STANDARDSWatson Data
Platform
The Ecosystem

20. WATSON DATA PLATFORM
DEMO
HARSHA KAPRE– OFFERING MANAGER WATSON DATA PLATFORM
@harsha_kapre