Many data science teams struggle to capture the full value of AI for their organizations, due to challenges around data, skills and tooling. These barriers ultimately prevent organizations from using machine learning and deep learning to make better decisions and deliver better products or services. In this session, you’ll learn about tools that: – Provides one collaborative environment for all your team’s data and AI projects – Offers a choice of builder tools – both programming and visual – Includes familiar open source technology and frameworks https://databricks.com/session/making-data-and-ai-accessible-for-all

1. Armand Ruiz – IBM Watson
armand.ruiz@us.ibm.com
Making Data and AI
accessible for all
#SparkAISummit

2. Tools & Infrastructure
• Need an environment
that enables a “fail
fast” approach
• Discrete tools present
barriers to productivity
Governance
• If the data isn’t secure,
self-service isn’t a
reality
• Challenge
understanding data
lineage and getting to
a system of truth
Skills
• Data Science skills
are in low supply and
high demand
• Nurturing new data
professionals is
challenging
Data
• Data resides in silos
& difficult to access
• Unstructured and
external data wasn’t
considered
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Why are enterprises struggling
to capture the value of AI?
#SparkAISummit

3. Watson Studio: accelerating value from AI for enterprises
Watson Studio accelerates the machine and deep learning workflows required to infuse AI into
your business to drive innovation. It provides a suite of tools for data scientists, application
developers and subject matter experts to collaboratively and easily work with data and use that
data to build, train and deploy models at scale.
AI Requires Teamwork
• AI is not magic
• AI is algorithms + data + team
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4. Her Job:
Builds AI application that meet the
requirements of the business.
What she does:
• Starts PoCs which includes
gathering content, dialog
building and model training
• Focus is on app building for the
team or company to use. Will
handle ML Ops as needed
Sometimes known as:
Front-end, back-end, full stack,
mobile or low-code developer
Tanya
Domain Expert
Her Job:
To transfer knowledge to Watson for
a successful user experience.
What she does:
• Range of domain knowledge and
uses that to teach Watson and
develop a custom models
• As Tanya gains more experience
she optimizes her knowledge to
teach Watson to design better
end-user experiences.
Sometimes known as:
Subject matter expert, content
strategist.
His Job:
Transform data into knowledge for
solving business problems.
What he does:
•Runs experiments to build custom
models that solve business problems.
•Use techniques such as Machine
Learning or Deep Learning and works
with Tanya to validate success of
trained models.
Watson Studio
Built for AI teams – enabling team productivity and collaboration
Sometimes known as:
ML/DL engineer, Modeler, Data Miner
Ed
Data Engineer
His Job:
Architects how data is organized
and ensures operability
What he does:
• Builds data infrastructure and ETL
pipelines. Works with Spark,
Hadoop, and HDFS.
• Works with data scientist to
transform research models into
production quality systems.
Sometimes known as:
Data infrastructure engineer
Mike
Data Scientist
Deb
The Developer
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5. Watson Studio
Supporting the end-to-end AI workflow
3. Prepare Data
for Analysis
4. Build and Train
ML/DL Models
5. Deploy Models
6. Monitor, Analyze
and Manage
2. Search and Find
Relevant Data
1. Connect &
Access Data
Connect and
discover content
from multiple data
sources in the cloud
or on premises.
Bring structured
and unstructured
data to one toolkit.
Clean and prepare your
data with Data
Refinery, a tool to
create data preparation
pipelines visually.
Use popular open
source libraries to
prepare unstructured
data.
Democratize the
creation of ML and DL
models. Design your AI
models
programmatically or
visually with the most
popular open source
and IBM ML/DL
frameworks or leverage
transfer learning on
pre-trained models
using Watson tools to
adapt to your business
domain. Train at scale
on GPUs and
distributed compute
Deploy your models
easily and have them
scale automatically for
online, batch or
streaming use cases
Monitor the
performance of the
models in production
and trigger automatic
retraining and
redeployment of
models. Build
Enterprise Trust with
Bias Detection,
Mitigation Model
Robustness and
Testing Service Model
Security.
Find data (structured,
unstructured) and AI
assets (e.g., ML/DL
models, notebooks,
Watson Data Kits) in
the Knowledge
Catalog with intelligent
search and giving the
right access to the right
users.
5

6. Demo
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7. Watson Studio
Tools for supporting the end-to-end AI workflow
Model Lifecycle Management
Machine Learning Runtimes Deep Learning Runtimes
Authoring Tools
Cloud Infrastructure as a Service
• Most popular open source frameworks
• IBM best-in-class frameworks
• Create, collaborate, deploy, and monitor
• Best of breed open source & IBM tools
• Code (R, Python or Scala) and no-code/visual
modeling tools
• Fully managed service
• Container-based resource management
• Elastic pay as you go cpu/gpu power
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8. Date, Time, Location & Duration Session title and Speaker
Tue, June 5 | 11 AM
2010-2012, 30 mins
Productionizing Spark ML Pipelines with the Portable Format for Analytics
Nick Pentreath (IBM)
Tue, June 5 | 2 PM
2018, 30 mins
Making PySpark Amazing—From Faster UDFs to Dependency Management and Graphing!
Holden Karau (Google) Bryan Cutler (IBM)
Tue, June 5 | 2 PM
Nook by 2001, 30 mins
Making Data and AI Accessible for All
Armand Ruiz Gabernet (IBM)
Tue, June 5 | 2:40 PM
2002-2004, 30 mins
Cognitive Database: An Apache Spark-Based AI-Enabled Relational Database System
Rajesh Bordawekar (IBM T.J. Watson Research Center)
Tue, June 5 | 3:20 PM
3016-3022, 30 mins
Dynamic Priorities for Apache Spark Application’s Resource Allocations
Michael Feiman (IBM Spectrum Computing) Shinnosuke Okada (IBM Canada Ltd.)
Tue, June 5 | 3:20 PM
2001-2005, 30 mins
Model Parallelism in Spark ML Cross-Validation
Nick Pentreath (IBM) Bryan Cutler (IBM)
Tue, June 5 | 3:20 PM
2007, 30 mins
Serverless Machine Learning on Modern Hardware Using Apache Spark
Patrick Stuedi (IBM)
Tue, June 5 | 5:40 PM
2002-2004, 30 mins
Create a Loyal Customer Base by Knowing Their Personality Using AI-Based Personality Recommendation Engine;
Sourav Mazumder (IBM Analytics) Aradhna Tiwari (University of South Florida)
Tue, June 5 | 5:40 PM
2007, 30 mins
Transparent GPU Exploitation on Apache Spark
Dr. Kazuaki Ishizaki (IBM) Madhusudanan Kandasamy (IBM)
Tue, June 5 | 5:40 PM
2009-2011, 30 mins
Apache Spark Based Hyper-Parameter Selection and Adaptive Model Tuning for Deep Neural Networks
Yonggang Hu (IBM) Chao Xue (IBM)
IBM Sessions at Spark+AI Summit 2018 (Tuesday, June 5)
IBM Cloud / Watson and Cloud Platform / © 2018 IBM Corporation 8

9. Date, Time, Location & Duration Session title and Speaker
Wed, June 6 | 12:50 PM Birds of a Feather: Apache Arrow in Spark and More
Bryan Cutler (IBM) Li Jin (Two Sigma Investments, LP)
Wed, June 6 | 2 PM
2002-2004, 30 mins
Deep Learning for Recommender Systems
Nick Pentreath (IBM) )
Wed, June 6 | 3:20 PM
2018, 30 mins
Bringing an AI Ecosystem to the Domain Expert and Enterprise AI Developer
Frederick Reiss (IBM) Vijay Bommireddipalli (IBM Center for Open-Source Data & AI Technologies)
IBM Sessions at Spark+AI Summit 2018 (Wednesday, June 6)
IBM Cloud / Watson and Cloud Platform / © 2018 IBM Corporation 9
Meet us at IBM booth in the Expo area.
Get started today at datascience.ibm.com

10. Thank you
Armand Ruiz
(armand.ruiz@us.ibm.com)
Get started with a Watson Studio free trial by visiting:
ibm.co/watsonstudio
10#SparkAISummit