Oracle Database House Party_Oracle Machine Learning to Pick a Good Inexpensive Bottle of Wine

1. Copyright © 2020, Oracle and/or its affiliates Using Oracle’s Converged “AI” Database to Pick a Good but Inexpensive Bottle of Wine #converged-ai-database Brendan Tierney Oracle ACE Director, Author, etc. Jeff Smith PM, SQL Developer, REST, etc., Charlie Berger PM, Oracle Machine Learning Starts at 9 am ET

2. 2 Copyright © 2020, Oracle and/or its affiliates | Confidential: Internal/Restricted/Highly Restricted [Date] Brendan Tierney President, Oralytics Brendan Tierney is an Oracle ACE Director and Ground Breaker Ambassador. He is based in Dublin Ireland, and has been working with Oracle products since Oracle v5 Database, Forms 2.3, RPT, etc. Most of the past 27 years he has been working in the area of big data, analytics and machine learning, with a focus on building solutions. About your presenters: Charlie Berger Sr. Director Product Management, Machine Learning, AI and Cognitive Analytics Charlie has been working in the field of machine learning, statistical and data analysis for over 30 years. He joined Oracle in 1999 during the acquisition of Thinking Machines Corporation. @CharlieDataMine @brendantierney

3. 3 Copyright © 2020, Oracle and/or its affiliates | Confidential: Internal/Restricted/Highly Restricted [Date] Jeff Smith Master Product Manager Jeff is the Product Manager at Oracle for SQL Developer, SQLcl, SQL Developer Data Modeler, and REST Data Services (ORDS). Jeff has worked with database tools for business analysts, developers, and the DBAs for 20 years. About your presenters: @oraclesqldev @oraclesqlcl @oracleREST

6. Oracle Mission Statement “Our mission is to help people see data in new ways, discover insights, unlock endless possibilities” © 2020 Oracle - Portland OUG Training Day 10/22/2020Copyright © 2020 Oracle and/or its affiliates.

7. Using Oracle’s Converged “AI” Database to Pick a Good but Inexpensive Bottle of Wine Share a simple and fun use case: SQL Developer Import data Oracle Data Miner Explore data Identify key attributes Build predictive ML models Apply ML models for predictions APEX Further investigate insights and predictions SQLDEV/ORDS Deploy ML Model using Oracle REST API (optional)Copyright © 2020 Oracle and/or its affiliates.

12. * Coming soon Oracle Machine Learning Oracle Machine Learning extends Oracle Database(s) and enables users to build “AI” applications and analytics dashboards OML delivers powerful in-database machine learning algorithms, automated ML functionality, and integration with open source Python and R. Copyright © 2020 Oracle and/or its affiliates. Oracle Machine Learning OML Services* Model Deployment and Management, Cognitive Image and Text OML4SQL SQL API OML4Py* Python API OML4R R API OML Notebooks with Apache Zeppelin on Autonomous Database OML4Spark R API on Big Data Oracle Data Miner Oracle SQL Developer extension OML AutoML UI* Code-free interface on Autonomous Database

13. CLASSIFICATION Naïve Bayes Logistic Regression (GLM) Decision Tree Random Forest Neural Network SupportVector Machine Explicit Semantic Analysis CLUSTERING Hierarchical K-Means Hierarchical O-Cluster Expectation Maximization (EM) ANOMALY DETECTION One-Class SVM TIME SERIES Forecasting - Exponential Smoothing Includes popular models e.g. Holt-Winters with trends, seasonality, irregularity, missing data REGRESSION Linear Model Generalized Linear Model SupportVector Machine (SVM) Stepwise Linear regression Neural Network ATTRIBUTE IMPORTANCE Minimum Description Length Principal Comp Analysis (PCA) Unsupervised Pair-wise KL Div CUR decomposition for row & AI ASSOCIATION RULES A priori/ market basket PREDICTIVE QUERIES Predict, cluster, detect, features SQL ANALYTICS SQL Windows SQL Patterns SQL Aggregates Oracle Machine Learning Algorithms FEATURE EXTRACTION Principal Comp Analysis (PCA) Non-negative Matrix Factorization Singular Value Decomposition (SVD) Explicit Semantic Analysis (ESA) TEXT MINING SUPPORT Algorithms support text Tokenization and theme extraction Explicit Semantic Analysis (ESA) for document similarity STATISTICAL FUNCTIONS Basic statistics: min, max, median, stdev, t-test, F-test, Pearson’s, Chi-Sq, ANOVA, etc. R & PYTHON Third-party R & Python Packages through Embedded Execution Spark MLlib algorithm integration MODEL DEPLOYMENT & MONITORING SQL—1st Class Objects Oracle RESTful API (ORDS) OML Services X1 X2 A1 A2 A3 A4 A5 A6 A7 Includes support for Partitioned Models,Transactional data and aggregations, Unstructured data, Geo-spatial data, Graph data. etc, * Coming soon * Coming soon Copyright © 2020 Oracle and/or its affiliates.

14. Determine Best Wine for Under $30 Build machine learning (ML) model in ADW to predict good, inexpensive wines using in- Database OML algorithms BEGIN DBMS_DATA_MINING.CREATE_MODEL( model_name => 'Wine_CLASS_MODEL', mining_function => dbms_data_mining.classification, data_table_name => ''Wine_TRAIN_DATA', case_id_column_name => 'ID', target_column_name => 'POINTS_BIN', settings_table_name => 'Wine_build_settings'); END; / Apply ML model using SQL query to predict a likely good, inexpensive wine SELECT PREDICTION_PROBABILITY(Wine_CLASS_MODEL, 'GT_90_POINTS' USING 25 as PRICE, ‘MALBEC' as VARIETY, ‘SPAIN' as COUNTRY) FROM dual; Using Machine Learning SQL Model Build & Model Prediction 14 Copyright © 2020 Oracle and/or its affiliates.

15. CRISP-DM Methodology Six Major Steps https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_miningCopyright © 2020 Oracle and/or its affiliates. DATA UNDERSTANDING DATA PREPARATION MODELING EVALUATION DEPLOYMENT BUSINESS UNDERSTANDING

16. CRISP-DM Methodology Six Major Steps https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_miningCopyright © 2020 Oracle and/or its affiliates. DATA UNDERSTANDING Assemble the “right data” Data profiling • Data visualization • Univariate statistics/group by • Bi-variate statistics DATA PREPARATION Sampling/Stratified Algorithm req’d transforms • Auto Data Preparation • MissingValues, Binning, Normalization, etc. • Unstructured data • Aggregations Domain specific transforms • “Engineered Features” Features Selection MODELING Algorithm settings/defaults • Stratified sampling • Feature selection • Build model(s) EVALUATION Model evaluation Model comparison Model selection DEPLOYMENT In-DB ML model apply • Real-time ML apply • In-database, REST Embed methodology • Applications • Dashboards BUSINESS UNDERSTANDING Well-defined business problem

17. CRISP-DM Methodology Six Major Steps https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_miningCopyright © 2020 Oracle and/or its affiliates. DATA UNDERSTANDING Assemble the “right data” Data profiling • Data visualization • Univariate statistics/group by • Bi-variate statistics DATA PREPARATION Sampling/Stratified Algorithm req’d transforms • Auto Data Preparation • MissingValues, Binning, Normalization, etc. • Unstructured data • Aggregations Domain specific transforms • “Engineered Features” Features Selection MODELING Algorithm settings/defaults • Stratified sampling • Feature selection • Build model(s) EVALUATION Model evaluation Model comparison Model selection DEPLOYMENT In-DB ML model apply • Real-time ML apply • In-database, REST Embed methodology • Applications • Dashboards BUSINESS UNDERSTANDING Well-defined business problem *Automated and/or system defaults

27. Oracle Machine Learning Key Features: • Collaborative UI for data scientist and analysts • Packaged with Autonomous Databases • Quick start Example notebooks • Easy access to shared notebooks, templates, permissions, scheduler, etc. • OML4SQL • OML4Py coming soon • Supports deployment of OML models Machine Learning Notebooks included in Autonomous Databases Copyright © 2020 Oracle and/or its affiliates.

29. Deployment using APEX Oracle APEX to query and distribute insights and predictions Apply the Models to Predict “BestWines” Copyright © 2019 Oracle and/or its affiliates. Predictions Copyright © 2020 Oracle and/or its affiliates.

39. Relevant Resources/Links Oracle Machine Learning Blog with 3 Hands-on Labs https://blogs.oracle.com/machinelearning/ • Hands-0n Lab: How to Pick a Good Wine for $30< using Oracle Autonomous Database, Oracle Machine Learning, APEX, Oracle Analytics Cloud and REST Services • Learn How to Use Oracle Data Miner UI in 45 Minutes • Hands-on Lab: Learn to Use Oracle Machine Learning Notebooks OML on Autonomous Database Workshop https://oracle.github.io/learning-library/workshops/adwc4dev/?lab=introduction Oracle Machine Learning Overview: From Oracle Data Professional to Oracle Data Scientist! https://www.youtube.com/watch?time_continue=1&v= jFBMhOapGL8&feature=emb_logo

44. ThankYou Charlie Berger, charlie.berger@oracle.com @CharlieDataMine Jeff Smith, jeff.d.smith@oracle.com, @oraclesqldev @oraclesqlcl @oracleREST Brendan Tierney, brendan.tierney@oralytics.com, @brendantierney

45. 46 Copyright © 2020, Oracle and/or its affiliates | Confidential: Internal/Restricted/Highly Restricted [Date] Coming up at 10am ET… Spatial Technologies - @Home and Everywhere Else on the Map with David Lapp & Tim Vlamis Break time! Remember to take the Dev Gym quizzes to receive your certificate of completion. Also, please complete the feedback form for the previous session by 10:15am ET … links in the Zoom chat

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