SQL ANALYTICS – Spark Mllib Algorithm Integration E.G

Oracle Machine Learning & Advanced Analytics Data Management Platforms Move the Algorithms; Not the Data! Charlie Berger, MS Engineering, MBA Sr. Director Product Management, Machine Learning, AI and Cognitive Analytics <a href="mailto:[email protected]" target="_blank">[email protected] </a><a href="/goto?url=http://www.twitter.com/CharlieDataMine" target="_blank">www.twitter.com/CharlieDataMine </a>Copyright © 2018, 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 © 2018, Oracle and/or its affiliates. All rights reserved. | 2Machine Learning, Data Mining, Predictive Analytics Automatically sift through large amounts of data to find hidden patterns, discover new insights and make predictions A1 A2 A3 A4 A5 A6 A7 • Identify most important factor (Attribute Importance) • Predict customer behavior (Classification) • Predict or estimate a value (Regression) • Find profiles of targeted people or items (Decision Trees) • Segment a population (Clustering) • Find fraudulent or “rare events” (Anomaly Detection) • Determine co-occurring items in a “baskets” (Associations) Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | Oracle’s Data Management and Machine Learning Market Observations: • Machine learning, predictive analytics & “AI” now must-have requirements • Separate islands for data management and data science just don’t work • Enterprises whose data science teams most rapidly extract insights and predictions win Conclusions: • Must “operationalize” ML insights and predictions throughout enterprise • Multilingual Machine Learning: SQL, R, Python, Workflow UI, Notebooks, Embed ML in Appls, • Evolving towards combined Data Management + Machine Learning environment that can essentially to manage and “think” about data <ul style="display: flex;"><li style="flex:1">Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | </li><li style="flex:1">4</li></ul>Oracle’s Data Management and Machine Learning Architectural Strategy • In-Database proprietary implementations of machine learning algorithms • Leverage strengths of the Database and adds new ML tech – Counting, conditional probabilities, sort, rank, partition, group-by, collections, etc. – Parallel execution, bitmap indexes, partitioning, aggregations, recursion w/in parallel infrastructure, IEEE float, frequent itemsets, Automatic Data Preparation (ADP), Text processing, etc. • Focus on intelligent ML defaults, simplification & automation to enable applications – ADP, xforms, binning, missing values, Prediction_Details, Predictive_Queries, Model_views • Machine learning models built via PL/SQL script; scored via SQL functions (1st class DB objects) select cust_id from customers True power evident when scoring where region = ‘US’ and prediction_probability(churnmod, ‘Y’ using *) > 0.8; models using SQL functions, e.g. – “Smart scan” ML model scoring “push down”; Supports OLTP and ATPC environments • Machine Learning and Advanced Analytics are peer to rest of Oracle Data Mgmt features – Security, Back-up, Encryption, Scalability, “Big Data” eco-system, BDA, Big Data SQL, Cloud, Spark, etc. – Best ML & analytical development and deployment platform <ul style="display: flex;"><li style="flex:1">Copyright © 2016 Oracle and/or its affiliates. All rights reserved. </li><li style="flex:1">5</li></ul>Oracle’s Machine Learning & Advanced Analytics Fastest Way to Deliver Enterprise-wide Predictive Analytics Traditional ML Oracle’s in-DB Machine Learning Major Benefits Data Import Data Mining . Data remains in Database & Hadoop . Model building and scoring occur in-database . Use R packages with data-parallel invocations Model “Scoring” Data Prep. & Transformation avings . Leverage investment in Oracle IT . Eliminate data duplication Data Mining Model Building . 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 Model “Scoring” Embedded Data Prep Model Building Data Extraction Data Preparation Secs, Mins or Hours Hours, Days or Weeks Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Oracle’s Machine Learning & Adv. Analytics Algorithms <ul style="display: flex;"><li style="flex:1">CLASSIFICATION </li><li style="flex:1">REGRESSION </li><li style="flex:1">FEATURE EXTRACTION </li></ul><ul style="display: flex;"><li style="flex:1">– Naïve Bayes </li><li style="flex:1">– Linear Model </li><li style="flex:1">– Principal Comp Analysis (PCA) </li></ul>– Non-negative Matrix Factorization – Singular Value Decomposition (SVD) – Explicit Semantic Analysis (ESA) – Logistic Regression (GLM) – Decision Tree – Generalized Linear Model – Support Vector Machine (SVM) – Stepwise Linear regression – Neural Network – Random Forest – Neural Network TEXT MINING SUPPORT – Support Vector Machine – Explicit Semantic Analysis – LASSO – Algorithms support text type – Tokenization and theme extraction – Explicit Semantic Analysis (ESA) for document similarity A1 A2 A3 A4 A5 A6 A7 ATTRIBUTE IMPORTANCE CLUSTERING – Minimum Description Length – Principal Comp Analysis (PCA) – Unsupervised Pair-wise KL Div – CUR decomposition for row & AI – Hierarchical K-Means – Hierarchical O-Cluster – Expectation Maximization (EM) STATISTICAL FUNCTIONS – Basic statistics: min, max, median, stdev, t-test, F-test, Pearson’s, Chi-Sq, ANOVA, etc. <ul style="display: flex;"><li style="flex:1">ANOMALY DETECTION </li><li style="flex:1">ASSOCIATION RULES </li></ul><ul style="display: flex;"><li style="flex:1">– One-Class SVM </li><li style="flex:1">– A priori/ market basket </li></ul><ul style="display: flex;"><li style="flex:1">TIME SERIES </li><li style="flex:1">PREDICTIVE QUERIES </li><li style="flex:1">R PACKAGES </li></ul>– State of the art forecasting using Exponential Smoothing. <ul style="display: flex;"><li style="flex:1">– Predict, cluster, detect, features </li><li style="flex:1">– CRAN R Algorithm Packages </li></ul>through Embedded R Execution – Spark MLlib algorithm integration SQL ANALYTICS – Includes all popular models e.g. Holt-Winters with trends, seasons, irregularity, missing data – SQL Windows, SQL Patterns, SQL Aggregates EXPORTABLE ML MODELS • OAA (Oracle Data Mining + Oracle R Enterprise) and ORAAH combined – REST APIs for deployment • OAA includes support for Partitioned Models, Transactional, Unstructured, Geo-spatial, Graph data. etc, Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Multiple Data Scientist User Roles Supported Oracle’s Machine Learning/Advanced Analytics Additional relevant data Historical or Current Data to be “scored” for predictions and “engineered features” Oracle Database 12c <ul style="display: flex;"><li style="flex:1">Historical data </li><li style="flex:1">Assembled </li><li style="flex:1">Predictions & Insights </li></ul>historical data Sensor data, Text, unstructured data, transactional data, spatial data, etc. <ul style="display: flex;"><li style="flex:1">DBAs </li><li style="flex:1">Application Developers </li></ul><ul style="display: flex;"><li style="flex:1">OML Notebook Users </li><li style="flex:1">R, Python Users, Data Scientists </li><li style="flex:1">Data Analysts, Citizen Data Scientists </li></ul>Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | OAA Model Build and Real-time SQL Apply Prediction Simple SQL Syntax ML Model Build (PL/SQL) begin dbms_data_mining.create_model('BUY_INSUR1', 'CLASSIFICATION', 'CUST_INSUR_LTV', 'CUST_ID', 'BUY_INSURANCE', CUST_INSUR_LTV_SET'); end; /Model Apply (SQL query) Select prediction_probability(BUY_INSUR1, 'Yes' USING 3500 as bank_funds, 825 as checking_amount, 400 as credit_balance, 22 as age, 'Married' as marital_status, 93 as MONEY_MONTLY_OVERDRAWN, 1 as house_ownership) from dual; Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Fraud Prediction Demo Automated In-DB Analytical Methodology drop table CLAIMS_SET; exec dbms_data_mining.drop_model('CLAIMSMODEL'); create table CLAIMS_SET (setting_name varchar2(30), setting_value varchar2(4000)); insert into CLAIMS_SET values ('ALGO_NAME','ALGO_SUPPORT_VECTOR_MACHINES'); insert into CLAIMS_SET values ('PREP_AUTO','ON'); commit; begin dbms_data_mining.create_model('CLAIMSMODEL', 'CLASSIFICATION', 'CLAIMS', 'POLICYNUMBER', null, 'CLAIMS_SET'); end; /Automated Monthly “Application”! Just -- Top 5 most suspicious fraud policy holder claims select * from (select POLICYNUMBER, round(prob_fraud*100,2) percent_fraud, rank() over (order by prob_fraud desc) rnk from (select POLICYNUMBER, prediction_probability(CLAIMSMODEL, '0' using *) prob_fraud from CLAIMS add: Create View CLAIMS2_30 As Select * from CLAIMS2 Where mydate > SYSDATE – 30 where PASTNUMBEROFCLAIMS in ('2to4', 'morethan4'))) where rnk <= 5 order by percent_fraud desc; Time measure: set timing on; Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | 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) Oracle Cloud 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 Likelihood to respond: Call Center Branch Office Get Advice RMobile Web Email Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Manage and Analyze All Your Data Data Scientists, R Users, Citizen Data Scientists Architecturally, Many Options and Flexibility SQL / R Boil down the Data Lake “Engineered Features” Big Data SQL / R – Derived attributes that reflect domain knowledge—key to best models e.g.: • Counts Object Store • Totals • Changes over time <ul style="display: flex;"><li style="flex:1">Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | </li><li style="flex:1">12 </li></ul>OAA Algorithm Performance <ul style="display: flex;"><li style="flex:1">Algorithm </li><li style="flex:1">Time (sec.) </li><li style="flex:1">Number of Attributes </li></ul>SVM SGD Solver GLM Classification SVM IPM Solver GLM Regression K-Means 83s 180s 811s 59s 900 9000 900 900 <ul style="display: flex;"><li style="flex:1">168s </li><li style="flex:1">9000 </li></ul>• In-memory 640 million rows, Airline On-Time dataset • SPARC M8-2 hardware • Courtesy of the SPARC Performance Team Copyright © 2018 Oracle and/or its affiliates. All rights reserved. | OAA Algorithm Scalability: Rows and Parallelism Random Forest 200 180 160 140 120 100 80 60 40 20 0<ul style="display: flex;"><li style="flex:1">100 </li><li style="flex:1">150 </li><li style="flex:1">200 </li><li style="flex:1">250 </li><li style="flex:1">300 </li><li style="flex:1">350 </li><li style="flex:1">400 </li><li style="flex:1">450 </li><li style="flex:1">500 </li><li style="flex:1">550 </li></ul>Row Count (M records) • X6-2 hardware • Benchmarks from Oracle Labs, Systems Research Group <ul style="display: flex;"><li style="flex:1">Copyright © 2018 Oracle and/or its affiliates. All rights reserved. | </li><li style="flex:1">14 </li></ul>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 <ul style="display: flex;"><li style="flex:1">Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | </li><li style="flex:1">15 </li></ul>The Core Ingredients of Good Machine Learning <ul style="display: flex;"><li style="flex:1">Domain Knowledge + Data Machine Learning Algorithms </li><li style="flex:1">Insights, Predictions </li></ul>Machine Learning Algorithms A1 A2 A3 A4 A5 A1 A2 A3 A4 A5 A6 A7 A6 A7 Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Deployment!! Most Important Factor in Machine Learning? Machine Learning Algorithms A “Thinking” Database A1 A2 A3 A4 A5 A6 A7 Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Introducing: Oracle Machine Learning SQL Notebook Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Introducing Oracle Autonomous Data Warehouse Cloud Value Proposition <ul style="display: flex;"><li style="flex:1">Easy </li><li style="flex:1">Fast </li><li style="flex:1">Elastic </li></ul>• Provision a data warehouse in as little <ul style="display: flex;"><li style="flex:1">• Up to 14x performance advantage than </li><li style="flex:1">• Only Pay for What you Use with user </li></ul>defined sizing, on-demand scaling & idle shut-off as 15-seconds • Automated management of database Redshift1 • High concurrency supports multi-user administration • Simple Load and Go with Automated access and workloads • Based on Exadata for extreme • Independent scaling of compute and storage Tuning <ul style="display: flex;"><li style="flex:1">performance </li><li style="flex:1">• Instant scaling with zero downtime </li></ul>• Dedicated cloud-ready migration tools including Redshift <ul style="display: flex;"><li style="flex:1">Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | </li><li style="flex:1">19 </li></ul>Confidential – Oracle Oracle Autonomous Data Warehouse Cloud Key Features Highly Elastic High-Performance Queries Independently scale compute and storage, without having to overpay for fixed blocks of resources and Concurrent Workloads Optimized query performance with preconfigured resource profiles for different types of users Oracle Machine Learning Oracle SQL Built-in Web-Based SQL ML Tool Apache Zeppelin Oracle Machine Learning notebooks ready to run ML from browser Autonomous DW Cloud is compatible with all business analytics tools that support Oracle Database Database migration utility Self Driving Dedicated cloud-ready migration tools for easy migration from Amazon Fully automated database for self-tuning patching and upgrading itself while the system is running Redshift, SQL Server and other databases Cloud-Based Data Loading Fast, scalable data-loading from Oracle Object Store, AWS S3, or on-premises Enterprise Grade Security Data is encrypted by default in the cloud, as well as in transit and at rest <ul style="display: flex;"><li style="flex:1">Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | </li><li style="flex:1">20 </li></ul>Confidential – Oracle ADWC + Oracle Machine Learning Notebooks Developer Analytics Autonomous Data Warehouse Cloud Tools Data Warehouse Services (EDWs, DW, departmental marts and sandboxes) <ul style="display: flex;"><li style="flex:1">Service Management </li><li style="flex:1">Built-in Access Tools </li></ul>Oracle Exadata Cloud Service Oracle Analytics Cloud Oracle SQL Developer Oracle Database Cloud Service Service Console Oracle Machine Learning Express Cloud Service Data Integration Services Oracle Data Integration Platform Cloud Autonomous Database Cloud 3rd Party DI on Oracle Cloud Compute Oracle Object Storage Cloud Flat Files and Staging 3rd Party DI On-premises <ul style="display: flex;"><li style="flex:1">Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | </li><li style="flex:1">21 </li></ul>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 Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | 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 Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Oracle’s Machine Learning & Adv. Analytics Algorithms <ul style="display: flex;"><li style="flex:1">CLASSIFICATION </li><li style="flex:1">REGRESSION </li><li style="flex:1">FEATURE EXTRACTION </li></ul><ul style="display: flex;"><li style="flex:1">– Naïve Bayes </li><li style="flex:1">– Linear Model </li><li style="flex:1">– Principal Comp Analysis (PCA) </li></ul>– Non-negative Matrix Factorization – Singular Value Decomposition (SVD) – Explicit Semantic Analysis (ESA) – Logistic Regression (GLM) – Decision Tree – Generalized Linear Model – Support Vector Machine (SVM) – Stepwise Linear regression – Neural Network – Random Forest – Neural Network STATISTICAL FUNCTIONS – Basic statistics: min, max, median, stdev, t-test, F-test, Pearson’s, Chi-Sq, ANOVA, etc. – Support Vector Machine – Explicit Semantic Analysis A1 A2 A3 A4 A5 A6 A7 ATTRIBUTE IMPORTANCE – Minimum Description Length – Principal Comp Analysis (PCA) – Unsupervised Pair-wise KL Div CLUSTERING – Hierarchical K-Means – Hierarchical O-Cluster – Expectation Maximization (EM) ASSOCIATION RULES ANOMALY DETECTION – A priori/ market basket – One-Class SVM

SQL ANALYTICS – Spark Mllib Algorithm Integration E.G

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