Are you seeing benefits from big data, AI and machine learning? Some companies are challenged by the complexity of the tools, access to quality data and the ability to operationalize these technologies. IBM’s Watson Studio addresses the needs of developers, data scientists and business analysts – who need to create, train and deploy machine and deep learning models, analyze and visualize data – all in an easy-to-use platform. Watson Studio supports Apple’s Core ML with Watson Visual Recognition service. 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. When coupled with IBM Watson Knowledge Catalog, it enables companies to create a secure catalog of AI assets including datasets, documents and models. In this session, you will learn how to use these new offerings to solve real world business problems and infuse AI into your business to drive innovation. Speaker Sumit Goyal, IBM, Software Engineer

1. IBM Cloud / Watson and Cloud Platform / © 2018 IBM Corporation
Simplifying AI and
Machine Learning
with Watson Studio
Sumit Goyal (@SumitG0yal)
Software Engineer

2. IBM Cloud / Watson and Cloud Platform / © 2018 IBM Corporation 2
About me
Sumit Goyal (@SumitG0yal)
Software Engineer, Watson Studio

3. 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
3
Why are enterprises struggling to
capture the value of AI?
IBM Cloud / Watson and Cloud Platform / © 2018 IBM Corporation

4. 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
IBM Cloud / Watson and Cloud Platform / © 2018 IBM Corporation 4

5. Watson: AI for Smarter Business
Watson Knowledge Catalog
Watson Business Solutions
Compliance
Assist
Customer
Care
Expert
Assist
Voice of
the
Customer
Watson Enriched Data &
Analytical Assets
Watson Powered Search &
Social Collaboration
Active Policy
Enforcement
Model Governance,
Traceability & Lineage
Data Kits
Learns from Small data
Watson Machine Learning and Deep Learning as a Service
Watson Studio
Search & Find
Relevant Data
Connect & Access
Data
Prepare Data
(Ingest, Curate,
& Enrich)
Build & Train
AI Models
Deploy
AI Models
Monitor, Analyze,
Manage
Continuous Learning
Watson APIs allow Applications to use AI and ML Models
Watson
Assistant
Watson
Cybersecurity
Compare &
Comply
Watson Applications
... ......
ISV & Third Party Applications

6. 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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7. Watson Studio
Supporting the end-to-end AI workflow
Prepare Data
for Analysis
Build and Train
ML/DL Models
Deploy Models
Monitor, Analyze
and Manage
Search and Find
Relevant Data
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.
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8. 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

9. Projects
Add Members
AI and Data Science as a Team Sport
Knowledge Catalog
Add/connect
Data
Reuse Assets
from Catalog
Publish Assets
to Catalog
Work with Data and AI
• Process/cleanse Data
• Create/run Notebooks
• Analyze/visualize Data
• Train AI/ML/DL Models
• Deploy Models
• …
as a team or individually
Create Project
Share Enterprise Assets
• Data
• Connections
• Notebooks
• Models
• Dashboards
• …
governed at scale
IBM
Cloud
Other
Clouds
On
Prem

10. Watson and Cloud Platform / March 2018 / © 2017 IBM Corporation
Watson Studio
• AI & data science as a team sport - Lets AI experts,
data scientists, analysts, stakeholders collaborate to
collect, share, explore, analyze data to derive insights,
train models, and share/deploy resulting assets
• Projects provide a secure environment in which teams
or individuals work with and analyze data using
• Connections to connect cloud and on prem data sources
• Refinery – clean and shape data for analysis/ ML
• Notebooks – Jupyter + Spark int., comments, versions,
share as link, GitHub int., PixieDust, EMR int., …
• Flows – create flows running on Spark or SPSS
• ML/DL – train with Spark ML, ScikitLearn, SPSS, …
and deploy to Watson Machine Learning service for prod
• AI Tools – Visual Recognition training, neural network
builder, and more to come …
• Dashboards – visual analytics and sharing of insights
• RStudio integrated with Spark
• Integration with AI and Data in many places
• Watson AI Services, IBM Analytics Engine, …
• IBM Cloud and on-prem data services
• Third party cloud and on-prem data services
• Built on IBM Cloud platform
Try it at https://www.ibm.com/cloud/watson-studio

11. Connect to Data in IBM Cloud Data Services, On Prem Data, or Data on other Clouds
Store your data in the IBM Cloud Connect to your on-prem data and third-party cloud data
... ...

12. Visualize
Represent analytic results as compelling interactive
graphics.
Discover
Utilize data assets from the entire organization with
confidence by accessing them through the Data
Catalog.
Share
Deliver dashboards across the enterprise where
analytical insights are required to make business
decisions.
Making Insights Available to All
Visualize and Analyze Data through Dashboards

13. • Interactive Documents that contain
insights and the code to achieve them
• Text to explain what the Notebook does and document insights
• Code cells for accessing, processing,
and analyzing data
• Result cells with output and visualizations
• Jupyter http://jupyter.org is the most popular open source
project and de facto standard
• Allows to create, run, edit notebooks
in web browsers
• Can run with Python, R, Scala, and
many other kernels
• Notebooks are stored as .ipynb JSON objects
• Not only for Data Science and AI, also useful for
other applications that benefit from
text + code + insights + viz in interactive docs
• Popular for publishing interactive tutorials & demos with
runnable code inside, and reproducable research
13
Analyze Data in Jupyter Notebooks

14. Train Machine Learning Models and deploy to Watson Machine Learning
Create & Train Models in
Watson Studio
- ML Flows
- ML Wizard
- ML Notebooks
ML Models
ML Flows
Watson
Machine
Learning
Service
Apps on
IBM CLoud
or other
Apps
REST
API to
call
Model
Create
& Train
Deploy Invoke
• Data Scientists can train ML models in Watson Studio using data in Projects to create and train models
• Use ML Wizard for assisted creation and training of models using common patterns and algorithms
• Use Notebooks or Flows to train models for more advanced use cases and more flexibility
• Deploy models to Watson Machine Learning (WML) service in Bluemix to run them in production
• Use WML REST API to invoke your models for online scoring / predictions

15. Upload ZIPs with training images
Typically having at least 10 images per class suffices
to get good results
Use the Visual Recognition Model
Get the REST API URL plus code snippets to invoke
the Visual Recognition model from your application
Train a Visual Recogniton Model
Pick the classes you want to include in training the models,
Watson Studio automatically connects to Watson Visual
Recognition to train with the selected data
Collect training data for Visual Recognition and
train/test Visual Recognition Models
Train Visual Recognition Models

16. Watson Knowledge Catalog
Discover
Intelligent discovery of data, advanced classification
and profiling to provide context
Govern
Powerful governance policy tools to control and
protect access to data with visibility to data use
Catalog
A rich metadata index of all data, with social
collaboration and enhanced findability
Unlock tribal knowledge to unleash your
data professionals, powered by AI

17. Watson Studio
Differentiating Capabilities
• Data Scientists, Subject Matter experts,
Business Analysts & Developers all in one
environment to accelerate innovation,
collaboration and productivity
• Built-in learning to get started or go the
distance with advanced tutorials
Integrated Collaboration Environment
• Best in-breed open source and IBM tools that
support the end-to-end AI lifecycle
• Choice of code or no-code tools to build and
train your own ML/DL models or easily train
and customize pre-trained Watson APIs
Choice of Tools for the full AI lifecycle
• Use Watson smarts and recommendations
for the best algorithms to use given your data,
OR
• Use the rich capabilities and controls to fine
tune your models
Support for all levels of expertise
• Monitor batch training experiments then
compare cross-model performance without
worrying about log transfers and scripts to
visualize results.
• You focus on designing your neural networks.
We’ll manage and track your assets.
Experiment centric DL workflow
• Deploy models into production then monitor
them to evaluate performance.
• Capture new data for continuous learning and
retrain models so they continually adapt to
changing conditions.
Model lifecycle & management
• Intelligent discovery of data and AI assets
that enables reuse & improves productivity
• Seamlessly integrated for productive use with
Machine Learning and Data science
• Powerful governance tools to control and
protect access to data
Integrated with Knowledge Catalog
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18. Demo
18IBM Cloud / Watson and Cloud Platform / © 2018 IBM Corporation
Try it at https://www.ibm.com/cloud/watson-studio