What’s Your Super-Power? Mine is Machine Learning with ......•Regular speaker at Oracle...

Jim CzuprynskiOracle ACE Director, ODTUG Database Community Lead + TechCeleration Editor

[email protected]/JimTheWhyGuy

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What’s Your Super-Power? Mine is Machine Learning with Oracle Autonomous DB

mailto:[email protected]

http://www.twitter.com/twitterhandle

Future and pastTechCasts:

Formerly called the BIWA Summit with the Spatial and Graph SummitSame great technical content…new name!

www.AnalyticsandDataSummit.org

Submit a topic to share at https://analyticsanddatasummit.org/techcasts/

My Credentials• 40 years of database-centric IT experience

• Oracle DBA since 2001

• Oracle 9i, 10g, 11g, 12c OCP and ADWC

• Oracle ACE Director since 2014

• ODTUG Database Committee Lead

• Editor of ODTUG TechCeleration

• Oracle-centric blog (Generally, It Depends)

• Regular speaker at Oracle OpenWorld, IOUG COLLABORATE, Hotsos Symposium, and Regional OUGs

➢E-mail me at [email protected]➢Follow me on Twitter (@JimTheWhyGuy)➢Connect with me on LinkedIn (Jim Czuprynski)

http://jimczuprynski.wordpress.com/presentations

mailto:[email protected]

Our Agenda

•Unlearn What You Have Learned: What Machine Learning (ML) Promises

•So … Just How Much ML Do I Really Need?

• Leveraging Built-In Autonomous DB ML Capabilities in Under 10 Minutes

•See the Difference? Using Visual Representation to Discover Hidden Patterns

•Meet OML’s Big Brother, Oracle Analytics Cloud (OAC)

If The Onion Recognizes a Trend … It’s Probably A Thing. Just Saying.

From The Onion, January 18, 2018

Let My DBAs Go: Freeing Your Team’s Best Data Experts From Tedium

Let My DBAs Go: Freeing Your Team’s Best Data Experts From Tedium

DBAs are often themost knowledgeableresources about your organization’s data. Who better to lead the transition to a

data-driven orientation?

Specialization Is Dead. Long Live the Generalist.

Compared to other scientists, Nobel laureates are at least twenty-two times more likely to

partake as an amateur actor, dancer, magician, or other type of performer.

- David J. Epstein. Range (p. 33).

“We now have the [enemy] exactly where we want them. We can now

attack in any direction.”- Brigadier General Anthony C. “Nuts” McAuliffe

Data is the new oil, and its miners are data scientists … but DBAs are uniquely

positioned to support themAll images from images.google.com

APEX, AI, and ML: Where Analytic Magic Happens

Application Express (APEX) makes it trivial to instantly import data and business

applications directly into Oracle … even if it’s just resident within a simple spreadsheet

Oracle’s REST API enables quick development of complex data

entry and reporting applications within APEX in a low-code

environment

Once relevant data is captured, Oracle’s built-in data mining tools

make is simple to build data models, apply well-known algorithms, and obtain predictions for immediate

business insights

From Nothing to OML, In Under Ten Minutes• Creating an OML User

• Organizing Your OML Desktop

• Building Your First Zeppelin Notebook

OML: Where Do You Want To Go Today?

See the full “cheat sheet” here: https://www.oracle.com/a/tech/docs/oml4sql-algorithm-cheat-sheet.pdf

https://www.oracle.com/a/tech/docs/oml4sql-algorithm-cheat-sheet.pdf

Configuring Your OML Environment

Request new ML User creation1


Request new ML User creation1 Specify username, password, and details2


Your user receives an e-mail confirmation 3


Your user receives an e-mail confirmation 3 If necessary, specify a new password4

Creating Your First Zeppelin Notebook

Welcome to the OML desktop …1


Welcome to the OML desktop …1 … from where you can create Projects, Workspaces, and Notebooks2


Create a new Project …3


Create a new Project …3 … specify your new Project’s details …4


Create a new Project …3 … specify your new Project’s details …4 … and then create your first Notebook5

Building an OML Analysis

First, let’s grant all necessary privileges to the TPC-E schema …

1


First, let’s grant all necessary privileges to the TPC-E schema …

1… and then build a query against several tables that capture Trade Details and ancillary information for a limited date range

2


… confirm retrieval execution …3


… confirm retrieval execution …3… and then view a sample of the data retrieved from TPC-E tables prior to analyses

4

Leveraging OML Data Visualization

Graphing the resulting retrieved data is as simple as picking a chart format (e.g. a simple pie chart)

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Graphing the resulting retrieved data is as simple as picking a chart format (e.g. a simple pie chart)

1Changing the graph’s format is as simple as dragging and dropping desired attributes into appropriate analytic regions of the interface2


Want to display results in a totally different graph format? Choose any one …

3


Want to display results in a totally different graph format? Choose any one …

3

… and even choose different values to graph

4

Leveraging DBMS_DATA_MINING (1)

Access Machine Learning tools …

1


Access Machine Learning tools …

1

… and choose from a number of available data mining examples and templates

2


Here’s an example of how to implement a Time Series forecast and its corresponding exponential smoothing algorithm …3


Here’s an example of how to implement a Time Series forecast and its corresponding exponential smoothing algorithm …3

… included within DBMS_DATA_MINING to implement data capture and then application of the algorithm

4


Here, we execute SQL statements necessary to create dependent objects for the Time Series analysis …

5


Here, we execute SQL statements necessary to create dependent objects for the Time Series analysis …

5 … using the following parameters specified for the algorithm6


Now let’s actually execute the Time Series algorithm …7


Now let’s actually execute the Time Series algorithm …7 … and view the results of the Time Series forecast8


Almost there! Here are the actual results of the forecast …9


Almost there! Here are the actual results of the forecast …9 … and the results of graphing them across time10


Here’s the tabular detail behind the graph …11


Here’s the tabular detail behind the graph …11 … and the result of applying key factor identification12


Here’s the end result, in graphic format

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Oh, Just One More Thing …• Leveraging Oracle Analytic Cloud (OAC) for deeper insights

• Examples: Analyzing Electoral Demographic Data

Personal or External Datasets

Enterprise Applications

Data Integration

Oracle Analytics Cloud

HOW? Analytical Data MartBusiness Managed Solution Architecture

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Ad hoc, Batch or

Scheduled

Business Leaders

Analysts

Data Scientists

Developers

OAC Dataflow, Manual or ETL

Any FunctionalData

Data Management

Autonomous Data Warehouse

Business Analytics

• Profitability• Attrition• Sentiment• Click Stream• Procurement• Usage Tracking

ERP CRM HCM

Makes data available for analytics

Self-service analytics with ML

Use Case: Managing a 2020 US Congressional Campaign

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• Identify potential voters, including “flippability” since 2018 election• Gerrymandered district stretches several dozen miles across five different counties and

56 municipalities

• Political landscape has changed dramatically in early months of 2020

• Analyze results of voter outreach• Record successful voter commitment to candidate

• Identify possible new volunteers and contributors

• Determine which messages resonate (retain!) … as well as which ones fall flat (discard!)

• Figure out how to best deploy multitudes of volunteers• Everyone is not suited to canvassing• Mundane but crucial tasks cannot be ignored!

• Handling requests for yard signs from motivated voters• Driving voter awareness through sign placement at high-traffic intersections

Realization: Perfect for the Cloud!

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• Relatively simple data model and small data volume (<500M rows)

• Short development time frames demand quick orchestration for compute and storage

• Complex n-dimensional analytics may be required• Past voting record vs. strength of party affiliation vs. impact of current

government initiatives vs. …• Determine “flippability” of voter before canvassing starts

• Security is a must … so encryption is crucial!• Most voter data is publicly available … but contributor and volunteer

information as well as canvassing results are proprietary• Removes concerns about on-premises development and data storage• Eliminates risk of penetration by rival campaign

Preparing Grouping Levels for Voter Demographics

SELECT

v_county_name

,v_situs_city

,v_situs_zip5

,v_age

,v_ethnicity

,v_race_designation

,v_likely_group

,v_likely_party

,COUNT(*)

FROM (

SELECT

v_county_name

,v_situs_city

,v_situs_zip5

,v_age

,v_race_designation

. . .

Add some higher-level

groupings for . . .


SELECT

v_county_name

,v_situs_city

,v_situs_zip5

,v_age

,v_ethnicity

,v_race_designation

,v_likely_group

,v_likely_party

,COUNT(*)

FROM (

SELECT

v_county_name

,v_situs_city

,v_situs_zip5

,v_age

,v_race_designation

. . .


groupings for . . .

. . .

,DECODE(v_race_designation

,'White (Low)' , 'White'

,'White (Medium)' , 'White'

,'White (High)' , 'White'

,'Hispanic (Low)' , 'Hispanic'

,'Hispanic (Medium)', 'Hispanic'

,'Hispanic (high)' , 'Hispanic'

,'Asian (Low)' , 'Asian'

,'Asian (medium)' , 'Asian'

,'Asian (high)' , 'Asian'

,'Black (Low)' , 'Black'

,'Black (Medium)' , 'Black'

,'Black (High)' , 'Black'

,'Unspecified'

) AS v_ethnicity

. . .

… ethnicity …


SELECT

v_county_name

,v_situs_city

,v_situs_zip5

,v_age

,v_ethnicity

,v_race_designation

,v_likely_group

,v_likely_party

,COUNT(*)

FROM (

SELECT

v_county_name

,v_situs_city

,v_situs_zip5

,v_age

,v_race_designation

. . .


groupings for . . .

. . .














,'Unspecified'

) AS v_ethnicity

. . .

… ethnicity …

. . .


,'White (Low)' , 'Low'

,'White (Medium)' , 'Median'

,'White (High)' , 'High'

,'Hispanic (Low)' , 'Low'

,'Hispanic (Medium)', 'Median'

,'Hispanic (high)' , 'High'

,'Asian (Low)' , 'Low'

,'Asian (medium)' , 'Median'

,'Asian (high)' , 'High'

,'Black (Low)' , 'Low'

,'Black (Medium)' , 'Nedian'

,'Black (High)' , 'High'

,'Unspecified'

) AS v_income_level

. . .

… income level …


SELECT

v_county_name

,v_situs_city

,v_situs_zip5

,v_age

,v_ethnicity

,v_race_designation

,v_likely_group

,v_likely_party

,COUNT(*)

FROM (

SELECT

v_county_name

,v_situs_city

,v_situs_zip5

,v_age

,v_race_designation

. . .


groupings for . . .

. . .














,'Unspecified'

) AS v_ethnicity

. . .

… ethnicity …

. . .


,'White (Low)' , 'Low'

,'White (Medium)' , 'Median'

,'White (High)' , 'High'

,'Hispanic (Low)' , 'Low'

,'Hispanic (Medium)', 'Median'

,'Hispanic (high)' , 'High'

,'Asian (Low)' , 'Low'

,'Asian (medium)' , 'Median'

,'Asian (high)' , 'High'

,'Black (Low)' , 'Low'

,'Black (Medium)' , 'Nedian'

,'Black (High)' , 'High'

,'Unspecified'

) AS v_income_level

. . .

… income level …

. . .

,v_likely_party

,DECODE(v_likely_party

,'LR', 'GOP’, 'SR', 'GOP’ ,'NR', 'GOP’

,'LD', 'DEM’, 'SD', 'DEM’, 'ND', 'DEM’

,'I' , 'IND'

,'Unspecified'

) AS v_likely_group

FROM vevo.voters

)

GROUP BY

v_county_name, v_situs_city, v_situs_zip5

,v_age, v_ethnicity, v_income_level, v_race_designation

,v_likely_group, ,v_likely_party

ORDER BY

v_county_name, v_situs_city, v_situs_zip5

,v_age, v_ethnicity, v_income_level, v_race_designation

,v_likely_group, ,v_likely_party

;

… and political party / orientation

Exploring Data With Oracle Analytics Cloud (1)

Choose a data source …1


Choose a data source …1 … and select which schema to gather data from2


Select which view to capture data from …3


Select which view to capture data from …3 … and view the columns available4


Here’s a preview of all selected columns from the chosen view …

5


Here’s a preview of all selected columns from the chosen view …

5 … and, if desired, a more complete look at all of the selected data from the view6


Now let’s move on to visualizing the data retrieved. Here are the data elements and their datatypes …

7


Now let’s move on to visualizing the data retrieved. Here are the data elements and their datatypes …

7 … and here are the myriad different presentation types we can select from to visualize the data

8


We’ll save the project for now …9


We’ll save the project for now …9 … and finally we can visualize our data within discrete categorizations10

Want To Get Off To A Quick Start? Check Out Our Brief Video Series.

And don’t miss this brief primer on how to leverage Autonomous DB and

Oracle Analytics Cloud (OAC):http://bit.ly/2u9qF6K

Here’s a short primer of how to leverage Oracle Machine Learning (OML) and

Zeppelin Notebooks to begin leveraging Machine Learning in a matter of minutes:

http://bit.ly/39FBVGX

http://bit.ly/2u9qF6K

http://bit.ly/39FBVGX

Useful Resources and Documentation

• OML Web Page: https://www.oracle.com/database/technologies/datawarehouse-bigdata/machine-learning.html

• OML “Cheat Sheet”: https://www.oracle.com/a/tech/docs/oml4sql-algorithm-cheat-sheet.pdf

• OML Blog Post: https://blogs.oracle.com/datamining/introducing-oracle-machine-learning-sql-notebooks-for-the-oracle-autonomous-data-warehouse-cloud

• Corresponding ODTUG Article Series:• Part 1: https://www.odtug.com/p/bl/et/blogid=20&blogaid=940

• Part 2: https://www.odtug.com/p/bl/et/blogid=20&blogaid=958

• Part 3: Coming soon!

https://www.oracle.com/database/technologies/datawarehouse-bigdata/machine-learning.html

https://www.oracle.com/a/tech/docs/oml4sql-algorithm-cheat-sheet.pdf

https://blogs.oracle.com/datamining/introducing-oracle-machine-learning-sql-notebooks-for-the-oracle-autonomous-data-warehouse-cloud

https://www.odtug.com/p/bl/et/blogid=20&blogaid=940

https://www.odtug.com/p/bl/et/blogid=20&blogaid=958

Other Helpful Links

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ORACLE AUTONOMOUS CLOUDhttps://cloud.oracle.com/tryit

ORACLE AUTONOMOUS HANDS ON LAB FOR DEVELOPERShttps://go.oracle.com/e/f2?LP=82486

ORACLE MACHINE LEARNING ON OTNhttps://www.oracle.com/technetwork/database/options/oml/overview/index.html

OML TUTORIALSBasic getting started: https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/omlug/get-started-oracle-machine-learning.html#GUID-2AEC56A4-E751-48A3-AAA0-0659EDD639BA

ORACLE ANALYTICS CLOUDExamples: https://www.oracle.com/solutions/business-analytics/data-visualization/examples.html

https://cloud.oracle.com/tryit

https://go.oracle.com/e/f2?LP=82486

https://www.oracle.com/technetwork/database/options/oml/overview/index.html

https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/omlug/get-started-oracle-machine-learning.html#GUID-2AEC56A4-E751-48A3-AAA0-0659EDD639BA

https://www.oracle.com/solutions/business-analytics/data-visualization/examples.html

Future and pastTechCasts:

Formerly called the BIWA Summit with the Spatial and Graph SummitSame great technical content…new name!

www.AnalyticsandDataSummit.org

Submit a topic to share at https://analyticsanddatasummit.org/techcasts/

What’s Your Super-Power? Mine is Machine Learning with ......•Regular speaker at Oracle...

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Transcript of What’s Your Super-Power? Mine is Machine Learning with ......•Regular speaker at Oracle...