How IBM technologies enable data science and AI

1. 1
Bringing AI to the Data
Sasha Lazarevic, IBM Switzerland
https://www.linkedin.com/in/lzrvc/
LZRVC.com
IBM Watson

2. 2
Barriers to deliver business value from data and AI
1. Lack of skills (data science, AI, microservices, DevOps, management)
2. Outdated infrastructure and applications
3. Data quality problems
4. Governance or regulatory issues
Dev Ops
Sec
AI

3. 3
IBM Approach to removing these barriers
1. Engagement Methodology
2. Data Science Methodology
3. IBM Data and AI Platform
4. Support Model

4. 4
Engagement Methodology – Watson Value Framework
Value Definition
and Measurement
 Step 1. Define what
success of the project
looks like to your
business and
stakeholders
 Step 2. Select up to 3
KPIs for measuring
progree towards your
success definition
 Step 3. Define how you
will measure each KPI
 Step 4. Establish KPI
baselines and
benchmarks
 Step 5. Observe,
measure and refine KPIs
Business Value Delivery Roadmap

5. 5
IBM Data Science Methodology
Source: https://www.ibmbigdatahub.com/blog/why-we-need-methodology-data-science

6. 6
IBM Data and AI Platform
ICpIBM
Cloud
Watson Studio & ICP for Data
NLU
NLC
Vision
WKS
Language
Speech
Watson
Assistant
Watson
Discovery
Watson
Compare and Comply
Customer Care
Risk and
Compliance
Knowledge
Worker
Process
Automation
Use Cases
Horizontal
Applications
Data
Science and
AI IDE
Operating
Infrastructure
IBM AI
OpenScale
Traceability
Explainability
Bias Mitigation
Prediction
Consistency
Continuous
Learning
IBM Q
WEX
SPSS
Developer
Tools

7. 7
IBM Cloud and ICp
1. IBM Cloud
 Bare metal, virtual servers, storage
 Containers, middleware, database services
 Workload migration services
 Global reach with more than 50 data centers
2. Swiss Banking Cloud
 Highly automated, Swiss-managed IaaS environment
 Modular and replicable
3. IBM Cloud private
 On premise with/without managed services
 Container platform based on Kubernetes

8. 8
IBM Cloud private and ICP for Data

9. 9
Watson Studio
Data + Algorithms + Team

10. 10
Watson Studio
Watson Studio Live Demo (https://dataplatform.cloud.ibm.com/)
 Watson Data Catalog
 Community Assets
 Data Refinery (Transformations, Visualizations)
 Jupyter Notebooks
 ML Flow Modeler (SPSS Modeler compatibility)
 Neural Network Modeler
 Team Collaboration and Access Controls

11. 11
Watson Studio
Machine Learning Model Compatibility
In Watson Studio
The same model in SPSS Modeler

12. 12
ICP for Data – Data Exploration
Demonstration Video - https://ibm.biz/BdYG9n

13. 13
ICP for Data – Data Transformation
Demonstration Video - https://ibm.biz/BdYG9n

14. 14
ICP for Data – Data Visualizations
Demonstration Video - https://ibm.biz/BdYG9n

15. 15
ICP for Data – Machine Learning Modeling
Demonstration Video - https://ibm.biz/BdYG9n

16. 16
ICP for Data – Machine Learning Modeling
SPSS Modeler Streams - Demonstration Video - https://www.youtube.com/watch?v=sAnwvo6i3GU

17. 17
New Watson Features
Watson Assistant (fka Conversation)
 Digressions – answer user’s question out of the context
 Disambiguation ("Did you mean __ ?")
 More response types : buttons, images, videos etc
 Integrations with Salesforce, Avaya, ServiceNo
 Log based chat builder (using human-to-human chats)
 Bot asset exchange
 Modularization of your assistant through Skills

18. 18
New Watson Features
Watson Voice Agent

19. 19
New Watson Features
Watson Discovery
 Document segmentation
 Connect to Sharepoint, Box, Salesforce …
 Excel is now supported in addition to pdf, word, ppt, json and html
 Smart document understanding

20. 20
New Watson Features
Compare and Comply
 Learn the contract structure and language
 Enable complex operations like comparison to other documents
 Use cases like: “Find all payment terms in a contract”, “Identify differences in terms between two
similar contracts”, “Compare contract with invoice”
User Interface
Watson Compare
and Comply
Watson
Discovery
Watson
Knowledge Studio
Business
Owner

21. 21
New Watson Features
AI OpenScale
 Data and model bias detection
 Logging for traceability to business outcomes
 Explainability of MK and DL models
 Instrumentation for business insights
 Business operation dashboard

22. 22
Watson on ICp
Watson Assistant
• GA Date: September 26, 2018
• Initial Capabilities:
• Classification (intents)
• Entities
• Dialog
• Full Functionality Planned for 1H-19
• Languages: All Public Cloud Languages
Watson Speech-to-Text: Customer Care
• GA Date: September 20, 2018
• Initial Capabilities:
• Speech Transcription
• Language/Acoustic Customization
• PCI Redaction
• Language Support:
• English, Japanese, Korean out-of-the-box
• Other currently supported languages may
require customization and/or account service
support to achieve desired quality levels.
Compare & Comply
• New Features: September 28, 2018
• Element Classification model
enhancements
• Comparison API
• Table Understanding
• OCR/scanned document intake
• Feedback API/Document Visualizer
Bringing AI to the Data

23. 23
Contactez-nous
Pierre Kauffmann
Cognitive Solutions Architect
kauffmann@ch.ibm.com
Jérôme de Nomazy
Watson Solutions Business Development Executive
jerome.de.nomazy@ch.ibm.com
IBM Watson

24. 24
Bringing AI to the Data
Sasha Lazarevic, IBM Switzerland
https://www.linkedin.com/in/lzrvc/
LZRVC.com
Thank you !
IBM Watson