Artificial Intelligence and Machine Learning with the Oracle Data Science Cloud

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Artificial Intelligence and Machine Learning with
the Oracle Data Science Cloud
Juarez Barbosa
Principal Developer Advocate – Oracle EMEA

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Safe Harbor Statement
The following is intended to outline our general product direction. It is intended for
information purposes only, and may not be incorporated into any contract. It is not a
commitment to deliver any material, code, or functionality, and should not be relied upon
in making purchasing decisions. The development, release, and timing of any features or
functionality described for Oracle’s products remains at the sole discretion of Oracle.

4. 4
Data Science Overview

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What is Data Science?
5
• Data science is an interdisciplinary field that
uses scientific methods, processes,
algorithms, and systems to extract knowledge
and insights from data.
• A data scientist’s responsibilities include
preparing data, exploring and visualizing data,
and building models using programming
languages like Python or R.
• Data science includes methods and
techniques such as Artificial Intelligence (AI),
Machine Learning (ML), and Deep Learning
(DL).
ARTIFICIAL INTELLIGENCE
A program that can sense, reason, act, and adapt
MACHINE LEARNING
Algorithms whose performance improves as
they are exposed to more data over time
DEEP LEARNING
Subset of ML in which
multi-layered neural
networks learn from vast
amounts of data
DATA SCIENCE

6. 61%Organizations identify machine learning as
the most significant data initiative for next
year**
$57.6BEstimated spend on AI and machine learning by 2021
compared to $12B in 2017*
Market Opportunity
5
$3.5-5.8TPotential annual AI-derived business value across 19 industries***
* Source: Deloitte, Machine learning: things are getting intense, 2017
** Source: 2018 Outlook: Machine Learning and Artificial Intelligence, A Survey of 1,600+ Data Professionals. MEMSQL
*** Source: Notes from the AI frontier: Applications and value of deep learning, Mckinsey Global Institute

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Data Science is Critical in Every Industry and Function
7
Government Retail Technology Automotive
Consumer Goods Media Communications Healthcare
Gaming Travel Energy Finance &
Insurance
HR
Finance
Sales
Tech and
Product
Marketing
Support
DATA
SCIENCE

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Sample Data Science Use Cases
8
Marketing
Response Models
Scheduled Jobs
Customer churn
APIs
Text sentiment
analysis
Reports
Lifetime Value
Apps
Computer vision
and image tagging
Apps
Transactional data
ETL
Scheduled Jobs
Forecasting
Reports
Risk management
with machine
learning
APIs
Recommendation
engines
APIs
Data discovery and
auditing
Reports

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Data Science is Critical to Digital Transformation
9
Build and enhance
products and services
Enable more efficient
operations and processes
Create new channels
and business models

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What’s Holding Companies Back?
10
Data Scientists
Cannot work
efficiently
App Developers
Cannot access usable
ML
IT Admins
Too much time on
support
• Lengthy waits for
resources and data
• Difficulty collaborating
with teammates
• Long delays of days or
weeks to deploy work
• Many tools to manage
• No access to well-trained
models
• Access points not flexible
for deployment in all
scenarios
• Scalability of deployment
left out to the app
developer
Business
Executives
Do not see full ROI
• Growing list of open source
tools
• Continually building and
updating environments
• Limited standardization
across workflows
• No transparency into work
• No model integration
with decision making
systems
• Unable to access or
share outputs
• Difficult to collaborate
with data scientists
Despite the promise of data science, and huge investments in data science teams, inefficient
workflows are holding companies back from realizing the full potential of machine learning.

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What’s the Impact?
11
Organizations are losing out on millions of dollars
in profits and cost savings from operations that
should be driven by machine learning

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Enterprise Data Science Requires a Comprehensive Platform
to Simplify Operations and Deploy Models at Scale
12
A robust, easy-to-use data science platform
removes barriers to deploying valuable
machine learning models in production by:
• Accelerating use of proper open source
tools, frameworks, and infrastructure
• Overcoming resource bottlenecks by
making work more self-service through a
collaborative platform
• Quickly leveraging predictive analytics to
drive positive business outcomes
Manage data
and tools
Collaborate
securely
Power
business
Work in standardized
environments

13. 13
Oracle Solution

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Oracle Data Science Cloud
14
The Oracle Data Science Cloud enables data science teams
to organize their work, easily access data and computing
resources, and build, train, deploy, and manage models on
the Oracle Cloud.
The Oracle Data Science Cloud makes data science teams
more productive, and enables them to deploy more work
faster to power their organizations with machine learning.
What
is It?
What’s
the
Value?
*Final name pending legal review and approval

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Oracle Data Science Cloud Workflow
15
Reproducibility
Data
Versioning
Code
Versioning
Model
Versioning
Environment
Management
Model Deployment
Operationalize Models as
Scalable APIs
Model Management
Monitor and Optimize Model
Performance
Data Exploration
Collaborative Data Analysis /
Feature Engineering
Model Build and Train
with Open Source Frameworks
Collaborators
∙ Data Scientists
∙ Business Stakeholders
∙ App Developers
∙ IT Admins

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End-to-end platform for enterprise data scientists
Oracle Data Science Cloud Core Capabilities
16
• Data science workflow: Collaboration for enterprise data science teams in projects
• Model building and training*: Python development in Jupyter notebooks
• Model deployment: Deploy models as APIs, serve predictions in real-time
• Version control: External Git Provider required for files
• Access to open-source: Curated sets of packages for data science use cases
• Access to compute: Self-service access to spin up containers on OKE Cluster of OCI VMs (CPU only)
• Access to data: Oracle Object Store
* Model training in single Jupyter container with reserved CPU/memory (non-distributed over multiple containers)

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Oracle Data Science Cloud Key Components & Benefits
17
Collaborative
Project driven UI enables teams to easily
work together on end-to-end modeling
workflows with self-service access to data
and resources
Integrated
Support for latest open source tools, version
control, and tight integration with OCI and
Oracle Big Data Platform
Enterprise-Grade
A fully managed platform built to meet the
needs of the modern enterprise
Core Benefits:
Oracle Data Science Cloud
Oracle PaaS & IaaS
Projects Notebooks
Open Source
Languages &
Libraries
Version
Control
Use Case
Templates
Model
Build & Train
Self-Service Scalable Compute (OCI)
Object
Store
Catalog
Data
Lake
Streaming
Autonomous
Data Warehouse
Model
Deployment
Model
Monitoring
Access
Controls &
Security

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Oracle Data Science Cloud is COLLABORATIVE
18
● Project-driven UI simplifies data science
operations and enables teams to work
together
● Built-in version control ensures all data,
code, and models can be tracked and
reproduced
● Granular access controls enable
managers or admins to control who has
access to projects and data
● Support for teams to collaboratively
build, train, deploy, and manage models
from a central workspace

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Oracle Data Science Cloud is INTEGRATED
19
● Platform supports a wide range of open
source tools, libraries, and languages to
tackle different use cases
● Native support for most popular version
control providers (Github, Gitlab, and
Bitbucket) ensures all work is synced
across the platform
● Tight integration with OCI and Oracle Big
Data Platform provides data scientists
with self-service access to scalable
compute and the data they need to get
to work quickly
Data Analysis,
ML, AI
Version ControlTools &
Languages
Visualization
Use the Best of Open Source
Easily Access Data and Compute
Streams
Batch
Data
Warehouse
NoSQL
Databases
Self-Service Scalable Compute (OCI)
Object
Store
Data
Lake
Spark Catalog

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Oracle Data Science Cloud is ENTERPRISE-GRADE
20
Fully Managed Highly Available
• Fully managed platform built on Kubernetes
• Platform is highly available — ensuring
anytime, anywhere availability and access
• Support for large teams with containerized
workloads, preventing resource contention
on a scalable cluster
• Integration with Oracle IDCS enables robust
access control management
• Designed to leverage high performance
Oracle Cloud Infrastructure
Scalable Secure
AD
1
AD
2
AD
3
Oracle PaaS
Oracle IDCS

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Innovative approach to
ModelOps/Management:
Comprehensive model
management from data
acquisition to model retirement
Oracle Data Science Cloud Future Vision: Key Themes
21
Automation across lifecycle:
Automation around model
development (AutoML) and post
deployment model management
Leading data science ecosystem:
AI APIs, data stores, use-case
playbooks, AI App integrations,
and more
Infinitely scalable
“horsepower”: Deep
integration with Oracle SaaS,
PaaS, IaaS to tackle any use case
at any scale with any data
Best-in-class modelops/management, scalable “horsepower,” automation, and a leading data science ecosystem