âOracle Machine Learningâ Big Data Cloud

The Naked Future:

What Happens in a World that Anticipates Your Every Move? Move the Algorithms; Not the Data! Charlie Berger, MS Engineering, MBA Sr. Director Product Management, Machine Learning, AI and Cognitive Analytics [email protected] www.twitter.com/CharlieDataMine Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |

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.

Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |

2

Today…

Google Now Provides Tailored Local Updates

• Get Droid alerts/updates: – Local news – Local weather – Your stated interests – Your sports teams – National news updates


Imagine these scenarios Meet old Girlfriend at Coffee Shop

• Wake up, read Droid alert about meeting a old girlfriend in the coffee shop who will be getting married, but hasn’t told anyone yet. – Change of address – Adopt a dog – Facebook pictures – Tweets – Online ring purchase by close contact

When you meet your old girlfriend at the coffee shop this morning, act surprised to learn she is getting married 


Imagine these scenarios Get called to pick up child at School

• Get Droid alert to pick up your child at school before she sits next to student with 75% chance of getting flu today and who has a elderly, fragile grandmother whose DNA shows she would be in severe risk. – Location and status – Distance – Relationships – Genomics, exposure health risks Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |

Predictive Analytics Not New • …Remember the Plague?  – Spreading throughout the Mediterranean and Europe, the Black Death is estimated to have killed 30–60% of Europe's total population.

• Remember more of the story?

https://en.wikipedia.org/wiki/Black_Death Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |

to today! Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |


The “Near Future”

Big Data, Interconnected World + Machine Learning

• “Datafication” of EVERYTHING • “Digital exhaust” – – – –

GPS Tweets Geo-tags Facebook •

posts, pics, friends

– LinkedIn – RFID – Medical records

http://terrificdata.com/2016/10/11/examples-big-data-applications/ Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |

Big Data, Interconnected World + Machine Learning The “Very Near Future” is already here

http://terrificdata.com/2016/10/11/examples-big-data-applications/ Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |

2001: A Space Odyssey The Dawn of Man scene

• Our adoption of machine learning and “artificial intelligence” is about at this stage —the BEGINNING!


Big Data Analytics Urban Myths Very possible, but not actually true

Targets Predicts Daughter’s Pregnancy

Beer & Diaper Story

Story told as, based on new purchase of “pregnancy related items”, Target sends “Congrats on your new baby” promos to house.

Told as either Walmart or Osco Drug noticed a correlation, found the pattern, moved beer and diapers closer and earned higher profits.

The rest of the story:

The rest of the story:

http://www.kdnuggets.com/2014/05/target-predict-teen-pregnancy-insidestory.html

http://www.dssresources.com/newsletters/66.php https://www.theregister.co.uk/2006/08/15/beer_diapers/ http://robotics.stanford.edu/~ronnyk/chasm.pdf


Big Data Notable Moments in Time Very true; perhaps not noticed at the time

IBM “Watson” Wins Jeopardy Deep Blue versus Garry Kasparov Was a two chess matches between world chess champ Garry Computer Wins on ‘Jeopardy!’: Trivial, It’s Not! Kasparov and IBM supercomputer Deep Blue.

Kasparov won the first match. The second in New York City in Source: 1997 was won by Deep Blue. http://www.nytimes.com/2011/02/17/science/17jeopardySource: watson.html?pagewanted=all https://en.wikipedia.org/wiki/Deep_Blue_versus_Garry_Kasparov Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |


Predicting Human Genome--1999

The Use of Pedometers to Predict Ovulation and Get Cows Pregnant--20015

..Generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute….

Source:

Source:

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

http://extension.psu.edu/animals/dairy/news/2014/the-use-of-activitymonitors-to-predict-ovulation-and-get-cows-pregnant http://www.ibtimes.co.uk/connected-cattle-how-wearables-cloud-helpfarmers-get-their-cows-pregnant-1499220


Big Data Notable Moments in Time Very true; perhaps not noticed at the time Personalized Gas Pump Advertising

Source: http://nationaloutdoormedia.com/gas-pump-top-advertising/

Using Tweets to Predict Your Location Machine learning to gather mountains of data in order to predict where people are mostly likely to be at certain times during the day—1.5 years in advance!

Source: http://www.cs.rochester.edu/~sadilek/publications/Sadilek-Krumm_FarOut_AAAI-12.pdf http://www.dailymail.co.uk/sciencetech/article-4523850/Interactive-map-NewYork-shows-crowd-mobility.html Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |

Big Data Notable Moments in Time HOW BIG DATA MINES PERSONAL INFO TO CRAFT FAKE NEWS AND MANIPULATE VOTERS • Cambridge Analytics During US Presidential Election (Ted Cruz) • Amazing precise and successful use case of “uplift modeling” – “45,000 likely Republican Iowa caucus-goers who needed a little “persuasion message” – “very low in neuroticism, quite low in openness and slightly conscientious” – Among other things, care about -Gun rights”

• Click—Jeffrey Jay Ruest, gender: male, and his GPS coordinates. http://www.newsweek.com/2017/06/16/big-data-mines-personal-info-manipulate-voters-623131.html Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | https://www.youtube.com/watch?v=n8Dd5aVXLCc&feature=youtu.be

Big Data Notable Moments in Time Very true; perhaps not noticed at the time Facial Emotion Detection

Skeptical

Happy

Health Predictions based on your DNA

Afraid

Unhappy

Angry

Happy

Source: www.23andme.com



Predict Buyers Remorse??

Environmental Impact Predictions based on Behaviors

Add this to your There is an 80 Cart? probability that you will regret this purchase. 

Source: https://www.oroeco.com/overview?catID=All&topCategory=true



The Core Ingredients of Good Machine Learning Domain Knowledge + Data Machine Learning Algorithms

A1 A2 A3 A4 A5 A6 A7


Insights, Predictions

Most Important Factor in Machine Learning? Deployment!!


Take a Brief Moment to Tweet about my #OOW Talk 


Machine Learning and Advanced Analytics Functionality Overview


Dilbert on Big Data


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Machine Learning/Analytics + Data Warehouse + Hadoop/Spark • Platform Sprawl • Inherent Problems – Complexity – Data Movement – Duplicated Data – Data Latency – Security exposures – Duplicated Storage – Duplicated Backups – Duplicated Systems – Dupicated Space and Power Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |

Traditional vs. Oracle Machine Learning/Predictive Analtyics • Traditional— “Move the data”

—“Don’t move the data!”


2

Traditional vs. Oracle Machine Learning/Predictive Analtyics • Traditional— “Move the data”

— “Move the algorithms”

Simpler, Smarter Data Management + Analytics / Machine Learning Architecture


Oracle’s Machine Learning/Advanced Analytics Fastest Way to Deliver Enterprise-wide Predictive Analytics Traditional Analytics

Major Benefits

Oracle Advanced Analytics

Data Import Data Mining Model “Scoring”

 Data remains in Database & Hadoop  Model building and scoring occur in-database  Use R packages with data-parallel invocations

 Leverage investment in Oracle IT

Data Prep. & Transformation

avings

Data Mining Model Building

 Eliminate data duplication  Eliminate separate analytical servers

Data Prep & Transformation

 Deliver enterprise-wide applications  GUI for ML/Predictive Analytics & code gen  R interface leverages database as HPC engine

Data Extraction

Model “Scoring” Embedded Data Prep Model Building Data Preparation

Hours, Days or Weeks Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |

Secs, Mins or Hours

Operationalizing and Embedding Analytics for Action How long does it take to put a defined model into operational use?

?

?

?

Length of time to put a model into production. Based on 141 respondents who stated they are doing this today


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Oracle’s Machine Learning/Advanced Analytics Fastest Way to Deliver Scalable Enterprise-wide Predictive Analytics Key Features  Parallel, scalable machine learning algorithms and R integration  In-Database + Hadoop—Don’t move the data  Data analysts, data scientists & developers  Drag and drop workflow, R and SQL APIs  Extends data management into powerful advanced/predictive analytics platform  Enables enterprise predictive analytics deployment + applications Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |

Oracle’s Machine Learning & Adv. Analytics Algorithms CLASSIFICATION

– Naïve Bayes – Logistic Regression (GLM) – Decision Tree – Random Forest – Neural Network – Support Vector Machine

CLUSTERING

– Hierarchical K-Means – Hierarchical O-Cluster – Expectation Maximization (EM)

ANOMALY DETECTION – One-Class SVM

TIME SERIES

REGRESSION

– Linear Model – Generalized Linear Model – Support Vector Machine (SVM) – Stepwise Linear regression – Neural Network – LASSO

ATTRIBUTE IMPORTANCE

FEATURE EXTRACTION

– Principal Comp Analysis (PCA) – Non-negative Matrix Factorization – Singular Value Decomposition (SVD) – Explicit Semantic Analysis (ESA)

TEXT MINING SUPPORT

– Algorithms support text type – Tokenization and theme extraction – Explicit Semantic Analysis (ESA) for document similarity

A1 A2 A3 A4 A5 A6 A7

– Minimum Description Length – Principal Comp Analysis (PCA) – Unsupervised Pair-wise KL Div – CUR decomposition for row & AI

STATISTICAL FUNCTIONS

– Basic statistics: min, max, median, stdev, t-test, F-test, Pearson’s, Chi-Sq, ANOVA, etc.

ASSOCIATION RULES

– A priori/ market basket

– Holt-Winters, Regular & Irregular, with and w/o trends & seasonal – Single, Double Exp Smoothing

PREDICTIVE QUERIES

– Predict, cluster, detect, features

R PACKAGES

– CRAN R Algorithm Packages through Embedded R Execution – Spark MLlib algorithm integration

SQL ANALYTICS

– SQL Windows, SQL Patterns, SQL Aggregates

EXPORTABLE ML MODELS

• OAA (Oracle Data Mining + Oracle R Enterprise) and ORAAH combined • OAA includes support for Partitioned Models, Transactional, Unstructured, Geo-spatial, Graph data. etc,

– C and Java code for deployment


Oracle’s Machine Learning/Advanced Analytics Platforms Machine Learning Algorithms Embedded in the Data Management Platforms “Analytics Producers”

“Analytics Consumers”

Data Scientists, R Users, Citizen Data Scientists

BI Analysts, Managers

Functional Users (HCM, CRM)

Data Management + Advanced Analytical Platform Big Data SQL

Big Data Cloud Service “Oracle Machine Learning” Big Data Cloud ORAAH—Machine Learning Algorithms, Statistical Functions + R Integration for Scalable, Parallel, Distributed Execution

Database Cloud “Oracle Machine Learning” Database Edition Machine Learning Algorithms, Statistical Functions + R Integration for Scalable, Parallel, Distributed, in-DB Execution


Manage and Analyze All Your Data Data Scientists, R Users, Citizen Data Scientists

Architecturally, Many Options and Flexibility

SQL / R Boil down the Data Like

Big Data SQL / R

Object Store

“Engineered Features” – Derived attributes that reflect domain knowledge—key to best models e.g: • Counts • Totals • Changes over time Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |

“Why Oracle? Because that’s where the data is!” – Larry Ellison, Executive Chairman and CTO of Oracle Corporation


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Build Predictive Models on an Attribute Oracle’s Machine Learning Accelerates New Possibilities Machine Learning Model

 Function(X1, X2, ….X)



Y (LTV_BIN); Probability


Build Predictive Models on an Attribute Oracle’s Machine Learning Accelerates New Possibilities Machine Learning Model

 Function(X1, X2, ….X)





Build Predictive Models on an Attribute Oracle’s Machine Learning Accelerates New Possibilities Machine Learning Models  Function(X1, X2, ….X)  Y2 (BankFunds





Build Predictive Models on an Attribute Oracle’s Machine Learning Accelerates New Possibilities Machine Learning Models  Function(X1, X2, ….X)  Y2 (BankFunds





Oracle Advanced Analytics 12.2

Unofficial

Model Build Time Performance OAA 12.2 Algorithms

Rows (Ms)

T7-4 (Sparc & Solaris) X5-4 (Intel and Linux) Model Build Time (Secs / Degree of Parallelism)

Attributes Importance

640

28s / 512

K Means Clustering Expectation Maximization

640 159

161s / 256 455s / 512

Wow! That’s Fast! 44s / 72

268s / 144 588s / 144

Naive Bayes Classification GLM Classification GLM Regression

320 17s / 256 23s / 72 640 154s / 512 363s / 144 640 55s / 512 93s / 144 In 24 hours, could build new predictive models for entire Support Vector Machine (IPM solver) 640 404sattributes, / 512 1411s / 144 United States Population, for 400 4 times! Support Vector Machine (SGD solver) 640 84s / 256 188s / 72 The way to read their results is that they compare 2 chips: X5 (Intel and Linux) and T7 (Sparc and Solaris). They are measuring scalability (time in seconds) with increase degree of parallelism (dop). The data also has high cardinality categorical columns translates inreserved. 9K mining Copyright © 2016, Oracle and/orwhich its affiliates. All rights | attributes (when algorithms require explosion). There are no comparisons to 12.1 and it is fair to say that the 12.1 algorithms could not run on data of this size.

“1-Click”, 100% Automated, SQL Predictive Queries Oracle’s Machine Learning Accelerates New Possibilities

• Predictive Queries • “1-Click” immediate machine learning model build AND model apply as a SQL query • Classification & regression – Multi-target problems • Clustering query • Anomaly query • Feature extraction query

“1-Click”  Predictions!

Automatically creates multiple anomaly detection models “Grouped_By” and “scores” by partition via powerful highly automated SQL query


Oracle Data Miner “workflow” UI Oracle SQL Developer extention; Easy to Use for “Citizen Data Scientist”

• Easy to use to define analytical methodologies that can be shared • SQL Developer Extension • Workflow API and generates SQL code for immediate deployment Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |

Oracle Cloud

ML Model Deployment for Real-Time Scoring Real-Time Scoring, Predictions and Recommendations • On-the-fly, single record apply with new data (e.g. from call center) Select prediction_probability(CLAS_DT_1_15, 'Yes' USING 7800 as bank_funds, 125 as checking_amount, 20 as credit_balance, 55 as age, 'Married' as marital_status, 250 as MONEY_MONTLY_OVERDRAWN, 1 as house_ownership) from dual;

Social Media Call Center

Likelihood to respond: Get AdviceBranch Office

R Mobile

Web Email


Oracle Cloud


R: Transparency via function overloading

Oracle Cloud

Invoke in-database aggregation function

> aggdata class(aggdata) [1] "ore.frame" attr(,"package") [1] "OREbase" > head(aggdata) Group.1 x 1 ABE 237 2 ABI 34 3 ABQ 1357 4 ABY 10 5 ACK 3 6 ACT 33

Oracle SQL select DEST, count(*) from ONTIME_S group by DEST

Oracle Database In-db Stats ONTIME_S

Database Server


Machine Learning


Oracle Machine Learning and Advanced Analytics Strategy and Road Map

• One server side product, with a single analytic library, supporting multiple data platforms, analytical engines, UIs and deployment strategies

GUI

SQL

R, Python, etc.

Data Miner, RStudio Notebooks

ML Algorithms Common core, parallel, distributed

Advanced Analytics

Big Data / Big Data Cloud

Relational

Oracle Database Cloud


DWCS

Oracle’s Machine Learning/Advanced Analytics Platforms Machine Learning Algorithms Embedded in the Data Management Platforms “Analytics Producers” Data Scientists, R Users, Citizen Data Scientists

New Zeppelin notebook based UI for data scientists collaborating and sharing ML analytical methodologies in Clouds Data Management + Advanced Analytical Platform Big Data SQL

Big Data Cloud Service “Oracle Machine Learning” Big Data Cloud ORAAH—Machine Learning Algorithms, Statistical Functions + R Integration for Scalable, Parallel, Distributed Execution

Database Cloud “Oracle Machine Learning” Database Edition Machine Learning Algorithms, Statistical Functions + R Integration for Scalable, Parallel, Distributed, in-DB Execution


Oracle Machine Learning Machine Learning Notebook for Autonomous Data Warehouse Cloud

Key Features • Collaborative UI for data scientists – Packaged with Autonomous Data Warehouse Cloud (V1) – Easy access to shared notebooks, templates, permissions, scheduler, etc. – SQL ML algorithms API (V1) – Supports deployment of ML analytics



5







Imagine these scenarios

Droid Suggest my Optimal “Next Activity”

Charlie, To optimize, tonight you should:

Charlie,

1. Enjoy a nice dinner at a 65% restaurant

To optimize, tonight you should: 1. Enjoy a nice dinner at a restaurant 65% 2. Go to Do-It-U-Self Depot for weekend project. 23% 3. Mac’n’Cheese +“How to” YouTube 97%

2. Go to Do-It-U-Self Depot for weekend project. 23% 3. Mac’n’Cheese + “How to Home Repair” YouTube 97%



Assemble the “Right Data” for Each Prediction • Purchases

• Demographics • Temporal data and changes in behaviors

• Networks • Location GPS

• Predictions as inputs Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |







Take a Brief Moment to Tweet about my #OOW Talk 

LinkedIn tagged as in my network Single Malt Whiskey fan 

Brendan Tierney, Oracle ACE, Author, Expert

Geo-tagged as San Francisco


Update 

Droid Suggests my Optimal “Next Activity”

Charlie, Update , to optimize, tonight you should: 1. Enjoy a single malt with friends!

Charlie,

95%

2. Catch up on work email

30%

3. See other OOW talks on ML

60%

Update  , to optimize, tonight you should: 1. Enjoy a single malt with friends! 95% 2. Catch up on work email 30% 3. See other OOW talks on ML 60%


Update 

Droid Suggests my Optimal “Next Activity”


“Minority Report”— Possible Sooner Than 2054? • It is like Minority Report—but with the “precogs” replaced by Oracle’s Machine Learning and AI and perhaps sooner than 2054


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Machine Learning & Advanced Analytics

Getting Started Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |

Getting Started—Oracle ML/AA Resources & Links Oracle Advanced Analytics Overview Information • Oracle's Machine Learning and Advanced Analytics 12.2c and Oracle Data Miner 4.2 New Features preso • Oracle Advanced Analytics Public Customer References • Oracle’s Machine Learning and Advanced Analytics Data Management Platforms white paper on OTN • Oracle INTERNAL ONLY OAA Product Management Wiki and Beehive Workspace (contains latest presentations, demos, product, etc. information) YouTube recorded Oracle Advanced Analytics Presentations and Demos, White Papers • Oracle's Machine Learning & Advanced Analytics 12.2 & Oracle Data Miner 4.2 New Features YouTube video • Library of YouTube Movies on Oracle Advanced Analytics, Data Mining, Machine Learning (7+ “live” Demos e.g. Oracle Data Miner 4.0 New Features, Retail, Fraud, Loyalty, Overview, etc.) • Overview YouTube video of Oracle’s Advanced Analytics and Machine Learning Getting Started/Training/Tutorials  Link to OAA/Oracle Data Miner Workflow GUI Online (free) Tutorial Series on OTN  Link to OAA/Oracle R Enterprise (free) Tutorial Series on OTN  Link to Try the Oracle Cloud Now!  Link to Getting Started w/ ODM blog entry  Link to New OAA/Oracle Data Mining 2-Day Instructor Led Oracle University course.  Oracle Data Mining Sample Code Examples

Send an email now to [email protected] and my “away message” will send you many of these links”

• • • • •

Additional Resources, Documentation & OTN Discussion Forums Oracle Advanced Analytics Option on OTN page OAA/Oracle Data Mining on OTN page, ODM Documentation & ODM Blog OAA/Oracle R Enterprise page on OTN page, ORE Documentation & ORE Blog Oracle SQL based Basic Statistical functions on OTN Oracle R Advanced Analytics for Hadoop (ORAAH) on OTN

Analytics and Data Summit , All Analytics, All Data, No Nonsense. •

March 20-22, 2018, Redwood Shores, CA Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |

www.biwasummit.org www.analyticsanddatasummit.org Copyright © 2017, Oracle and/or its affiliates. All rights reserved. |


Confidential – Oracle

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âOracle Machine Learningâ Big Data Cloud

âOracle Machine Learningâ Big Data Cloud

âOracle Machine Learningâ Big Data Cloud

âOracle Machine Learningâ Big Data Cloud