Machine Learning and AI at Oracle

VP AIOps for the Autonomous Database
Sandesh Rao
LAOUC
Machine Learning and AI at Oracle
@sandeshr
https://www.linkedin.com/in/raosandesh/
https://www.slideshare.net/SandeshRao4

Types of Machine Learning
Supervised Learning
Predict future outcomes with the help of training
data provided by human experts
Semi-Supervised Learning
Discover patterns within raw data and make
predictions, which are then reviewed by human
experts, who provide feedback which is used to
improve the model accuracy
Unsupervised Learning
Find patterns without any external input other
than the raw data
Reinforcement Learning
Take decisions based on past rewards for this type
of action

ML Project Workflow
Set the business objectives
Gather compare and
clean data
Identify and extract features
(important columns) from imported data
This helps us identify the efficiency of the
algorithm
Take the input data which is also called the training data
and apply the algorithm to it
In order for the algorithm to function efficiently, it is
important to pick the right value for hyper parameters
(input parameters to the algorithm)
Once the training data in the
algorithm are combined we
get a model
1
2
3
4
5

ML vs AutoML
Algorithm
Selection
Feature
Selection
Model
Tuning
Model
Evaluation
AutoML
automates the
manual steps
Accuracy
Repeated retraining cycles
Algorithms improve models as they get trained on greater
volumes of data or more recent/relevant data

Does not replace data scientists but
rather expediate their capabilities
Does AutoML remove the need for Data Scientists?
At the advent of the assembly line in manufacturing,
many tedious processes were automated.
This enabled workers to put their time and energy into
bigger issues, from quality of product to improving design
and manufacturing processes.
AutoML gives similar power to data scientists,
delivering more time to engineer predictive
features, develop data acquisition strategies,
improve the data transformation pipelines, and
more.

Copyright © 2021, Oracle and/or its affiliates
6
Oracle Machine Learning
Automated
Automated machine learning supports data scientist
productivity and empowers non-experts
Algorithm-specific data preparation, integrated text
mining, partitioned models
Scalable
Over 30 high performance, parallelized
in-database machine learning algorithms
that require no data movement
Production-ready
Quickly deploy and update machine learning models in
production via SQL and REST APIs
Deploy R and Python user-defined functions using
managed processes with easy data-parallel and task-
parallel invocation
Model
Repository
Workspaces
and Projects
Zeppelin-based
Notebooks
Model
Deployment
Model
Building
Model
Management
Prediction
Details
R and Python
Integration
AutoML
Data Management
Infrastructure
Oracle Database – Oracle Autonomous Database – Data Lake
Access – Integration – Preparation – Exploration
CPU – Storage – Network
Cloud On premises

Oracle Machine Learning interfaces to Oracle Database
Oracle
Autonomous Database
Oracle Database
OML Notebooks
Oracle Database
Cloud Service
OML4Py
Oracle Data Miner
OML4R
OML4SQL
Python client,
Jupyter Notebooks
SQL Developer
R client,
RStudio
SQL Developer
SQL*Plus
Data Management Platform
Oracle Machine Learning
Component
Tool
* coming soon
Apache Zeppelin
OML4SQL
OML4Py
OML4R*
Copyright © 2021 Oracle and/or its affiliates.

Most Productive for Developers and Analysts
Integrated microservices, events, REST, SaaS, ML, CI/CD, Low-Code
Supports diverse Workload
Transactions, analytics, ML, IoT, streaming, blockchain
Supports diverse Data
Relational, JSON, graph, spatial, text, OLAP, XML, multimedia
In cloud and on premises – integrated, not fragmented
Building on the world’s only converged database
Copyright © 2021, Oracle and/or its affiliates.

Oracle Machine Learning Notebooks
Collaborative UI
• Based on Apache Zeppelin
• Supports data scientists, data analysts,
application developers, and DBAs with
SQL and Python
• Easy notebook sharing
• Scheduling, versioning, access control
Included with Autonomous Database
• Automatically provisioned and managed
• In-database algorithms and
analytics functions
• Explore and prepare, build and evaluate
models, score data, deploy solutions
Autonomous Database as a Data Science Platform

CLASSIFICATION
• Naïve Bayes
• Logistic Regression (GLM)
• Decision Tree
• Random Forest
• Neural Network
• Support Vector Machine (SVM)
• Explicit Semantic Analysis
• XGBoost*
ANOMALY DETECTION
• One-Class SVM
• MSET-SPRT*
CLUSTERING
• Hierarchical K-Means
• Hierarchical O-Cluster
• Expectation Maximization (EM)
TIME SERIES
• Forecasting - Exponential Smoothing
• Includes popular models
e.g. Holt-Winters with trends,
seasonality, irregular time series
REGRESSION
• Generalized Linear Model (GLM)
• Support Vector Machine (SVM)
• Stepwise Linear regression
• Neural Network
• XGBoost*
ATTRIBUTE IMPORTANCE
• Minimum Description Length
• Principal Component Analysis (PCA)
• Unsupervised Pairwise KL Divergence
• CUR decomposition for row & AI
ASSOCIATION RULES
• A priori
PREDICTIVE QUERIES
• Predict, cluster, detect, features
SQL ANALYTICS
• SQL Windows
• SQL Patterns
• SQL Aggregates
FEATURE EXTRACTION
• Principal Comp Analysis (PCA)
• Non-negative Matrix Factorization
• Singular Value Decomposition (SVD)
• Explicit Semantic Analysis (ESA)
ROW IMPORTANCE
• CUR Decomposition
RANKING
• XGBoost*
TEXT MINING SUPPORT
• Algorithms support text columns
• Tokenization and theme extraction
• Explicit Semantic Analysis (ESA)
STATISTICAL FUNCTIONS
• min, max, median, stdev, t-test, F-test,
Pearson’s, Chi-Sq, ANOVA, etc.
Oracle Machine Learning Algorithms and Analytics in Oracle Database
* New in 21c
Includes support for Partitioned Models,
Transactional data and aggregations

Oracle Machine Learning for SQL
In-database, parallelized, distributed algorithms
• No extracting data to separate ML engine
• Fast and scalable
• Batch and real-time scoring at scale that leverages
Exadata storage-tier function pushdown
• Algorithm-specific automatic data preparation
• Explanatory prediction details
ML models as first-class database objects
• Access control per model
• Audit user actions
• Export / import models across databases
• Ease of backup, recovery, and security
Faster time-to-market through immediate solution deployment
Empower SQL users with immediate access to ML included with
Oracle Database and Oracle Autonomous Database
SQL Interfaces
SQL*Plus
SQLDeveloper
…
Oracle
Autonomous
Database
OML Notebooks
Oracle Database
with OML

OML to Predict Customer Behavior
-- Build a machine learning model to determine which customers are likely buy Travel Insurance
DECLARE
v_setlst DBMS_DATA_MINING.SETTING_LIST;
BEGIN
v_setlst('ALGO_NAME') := 'ALGO_SUPPORT_VECTOR_MACHINES';
V_setlst('PREP_AUTO') := 'ON';
DBMS_DATA_MINING.CREATE_MODEL2(
MODEL_NAME => 'BUY_TRVL_INSUR',
MINING_FUNCTION => 'CLASSIFICATION',
DATA_QUERY => 'select * from CUSTOMERS',
SET_LIST => v_setlst,
CASE_ID_COLUMN_NAME => 'CUST_ID',
TARGET_COLUMN_NAME => BUY_TRAVEL_INSURANCE');
END;
-- Apply a machine learning model to predict which customers are likely to buy
SELECT prediction_probability(BUY_TRVL_INSUR, 'Yes'
USING 3500 as bank_funds, 37 as age, 'Married' as marital_status, 2 as num_previous_cruises)
FROM dual;
Intuitive SQL API—OML4SQL

New algorithms and features
eXtreme Gradient Boosting Trees (XGBoost)
• Classification, regression, ranking
• Highly popular and powerful algorithm for speed and model accuracy
Multivariate State Estimation Technique- Sequential Probability Ratio Test (MSET-SPRT)
• Anomaly detection for sensors, IoT data sources
• Detects subtle anomalies while producing minimal false alarms
Neural Network
• Adam Solver - A minibatch solver – computationally efficient, requires little memory,
well-suited to larger data
• ReLU activation function – enables easier to train models with better performance
Enhanced prediction details
• Enables even higher quality understanding of factors that most contribute to a prediction
• For Support Vector Machine, Generalized Linear Model, Neural Network, k-Means
OML4SQL – new in Database 21c
13

Oracle Data Miner User Interface
SQL Developer Extension
for Oracle Database
on premises and DBCS
Automates typical data science
steps
Easy to use drag-and-
drop interface
Analytical workflows quickly
defined and shared
Wide range of algorithms and
data transformations
Generate SQL code for
immediate deployment
Create analytical workflows – productivity tool for data scientists – enables citizen data scientists

New algorithms and features
eXtreme Gradient Boosting Trees (XGBoost)
• Classification, regression, ranking
• Highly popular and powerful algorithm for speed and model accuracy
Multivariate State Estimation Technique- Sequential Probability Ratio Test (MSET-SPRT)
• Anomaly detection for sensors, IoT data sources
• Detects subtle anomalies while producing minimal false alarms
Neural Network
• Adam Solver - A minibatch solver – computationally efficient, requires little memory,
well-suited to larger data
• ReLU activation function – enables easier to train models with better performance
Enhanced prediction details
• Enables even higher quality understanding of factors that most contribute to a prediction
• For Support Vector Machine, Generalized Linear Model, Neural Network, k-Means
OML4SQL – new in Database 21c
15

Summary
• Minimize or eliminate data movement for database data
• Multi-persona, collaborative, democratized machine learning for data scientists, citizen data scientists, developers
• Multi-language API (SQL, Python) and no-code user interface
• Access from broader data lake data through external tables and Cloud SQL
• Data and model governance via Oracle Database and Autonomous Database security models in development and production
• Scalable and high-performance modeling and scoring
• Elastic scaling for machine learning as part of OML on Autonomous Database
• Model explainability and prediction details support XAI in development and production
• Bridges gap between development and production with model deployment options
• MLOps capabilities include immediate model production deployment from SQL and REST, user collaboration, queryable model repositories, and
support for streamlined creation of reproducible ML pipelines
• Oracle stack, SaaS, PaaS, IaaS provides a strong environment in which data engineers, ML engineers and architects, corporate developers and
others can contribute to the DS and ML workflow
• On-premises and Cloud availability for ML capabilities
• Oracle tools and enterprise applications integration, including Oracle Analytics Server, Oracle Analytics Cloud and Oracle APEX
• Simple pricing structure - ML capabilities included in core product at no additional cost
Oracle Machine Learning on ADB-S
16

Oracle Machine Learning for R and Python
Transparency layer
• Leverage proxy objects so data remains in database
• Overload native functions translating functionality to SQL
• Use familiar R / Python syntax on database data
Parallel, distributed algorithms
• Scalability and performance
• Exposes in-database algorithms available from OML4SQL
Embedded execution
• Manage and invoke R or Python scripts in Oracle Database
• Data-parallel, task-parallel, and non-parallel execution
• Use open source packages to augment functionality
OML4Py also includes AutoML and MLX
• Automated algorithm selection, feature selection, model tuning
• Algorithm-agnostic model explainability (MLX) for feature ranking
Empower data scientists with open source environments
Oracle
Database
SQL Interface
OML4R
OML Notebooks
OML4Py
REST Interface
Oracle
Autonomous
Database
Oracle
Database
SQL Interface

spawns
Embedded Execution
Example of parallel partitioned data flow using third party package using OML4Py
# user-defined function using sklearn
def build_lm(dat):
from sklearn import linear_model
lm = linear_model.LinearRegression()
X = dat[['PETAL_WIDTH']]
y = dat[['PETAL_LENGTH']]
lm.fit(X, y)
return lm
# select column(s) for partitioning data
index = oml.DataFrame(IRIS['SPECIES'])
# invoke function in parallel on IRIS table
mods = oml.group_apply(IRIS, index,
func=build_lm,
parallel=2)
mods.pull().items() OML4Py
Python Engine
OML4Py
Python Engine
OML4Py
OML Notebooks
REST Interface
Oracle Autonomous
Database
User tables

19

Enhance data scientist productivity and enable non-expert data professionals
Accelerate new ML projects
Automate repetitive and time-consuming tasks
Generate editable notebooks for selected models
Deploy models as REST endpoints
Featuring
• Monitor experiment progress
• Customize selection quality metric and metrics display
• Even faster data scoring performance for streaming
and real-time applications
OML AutoML UI
20

Simplify the machine learning modeling and deployment process
OML AutoML UI
OML
Model
Data
21
Auto Algorithm
Selection
• Identify in-database
algorithms likely to
achieve higher
model quality
• Find best algorithm
faster than exhaustive
search
Adaptive
Sampling
• Identify right sample
size for training data
• Adjust sample for
unbalanced data
Auto Feature
Selection
• De-noise data
• Reduce features by
identifying most
predictive
• Improve accuracy
and performance
Auto Model
Tuning
• Improves model
accuracy
• Automated tuning of
hyperparameters
• Avoid manual or
exhaustive search
techniques
OML AutoML UI Experiment Pipeline
Feature Prediction Impact
• Rank features most influential for scoring
• Algorithm-agnostic technique
• For each final model per algorithm
Plus…

Comparing OML4Py AutoML with OML AutoML UI
22
Step in workflow OML4Py AutoML API OML AutoML UI
Algorithm Selection ü Optional use ü
Adaptive Sampling Roadmap ü
Feature Selection ü Optional use ü
Model Tuning ü ü
Model Selection ü Specific API function to return top model
or user selection
ü Leaderboard ranks models
by score metric for use choice
Feature Prediction Impact ü Optional use via MLX ü
Generate notebook for model Not available ü
Integrated model deployment
to OML Services
Explicit model export and
REST API import
ü
Manual pipeline
assembly
Experiment assembles the
full pipeline

Oracle Machine Learning for Spark
Leverage Spark 2 environment for powerful
data preparation and machine learning
Use data across range of Data Lake sources
Achieve scalability and performance using
full Hadoop cluster
Parallelized and distributed ML algorithms from
native and Spark MLlib implementations
R Language API Component to Oracle Big Data Connectors and on Oracle Big Data Service
Java API
HDFS | Hive | Spark DF |
Impala | JDBC Sources
BDA
BDS
DIY
OML4Spark
R Client

Enable key elements of overall enterprise MLOps strategy
Fast data scoring performance for streaming and real-time applications
Pay only for actual scoring compute – no pre-provisioned VM
Facilitate collaboration across data science team
Model Management and Deployment Services
• Deploy in-database (native format) and third-party (ONNX format) models
• Import ONNX for Tensorflow, PyTorch, MXNet, scikitlearn, etc.
• Store, version, compare ML models
• Organize models within namespaces
Built-in cognitive text services
• Extract topics and keywords
• Sentiment analysis
• Text summary and similarity
OML Services
Supports lightweight model scoring using REST endpoints for application integration

25
Connectivity and use from Client
Oracle Machine Learning Services architecture
Copyright © 2021, Oracle and/or its affiliates. All rights reserved
REST
Client
user/pass
GET Token
Token + Actions
& Text/Objects
GET
POST
DELETE
Oracle Autonomous Database
/omlusers PDB
/omlmod OML Services

Components with built-in Oracle Machine Learning
Oracle Machine Learning Services - Methods
26
Repository
• Store Model
• Update Model
Namespace
• Model Listing
• Model Info
• Model Metadata
• Model Content
• Model
Admin
• Token using ADB user
and password
Generic
• Metadata for all
Versions: Version 1
Metadata
• Open API Specification
Deployment
• Create Model Endpoint
• Score Model using
Endpoint
• Endpoints
• Endpoint Details
• Open API Specification
for Endpoint
• Endpoint
Cognitive Text
• Get Most Relevant Topics
• Get Most Relevant
Keywords
• Get Summaries
• Get Sentiments
• Get Semantic Similarities
• Numeric Features
• Get Endpoints
GET
POST
DELETE
GET
POST
DELETE
GET
POST
GET
POST

Demo

28
OML components deployment scenarios

29
Prepared
Database
Table
Generate
notebook
{REST:API}
OML Services
Enterprise
Applications
Deploy in-database model
OML AutoML UI
Build
in-db model
Export and deploy
in-db model
In-database SQL scoring
Direct model access and In-
database SQL scoring
Direct model access and In-
database SQL scoring
Oracle APEX
In-database model deployment scenarios – OML AutoML UI

30
{REST:API}
OML Services
Oracle APEX
Deploy in-database model
Import
in-db model
SQL
OML Notebooks
Enterprise
Applications
Direct model access and
Export
in-db model
In-database model deployment scenarios – OML Notebooks
Direct model access and

31
Oracle Database
(on premises and DBCS)
(ADW, ATP, AJD)
(ADW, ATP, AJD)
Export and deploy in-db model
Export and deploy in-db model
Multi-database model deployment scenarios

32
Export model
in ONNX format
{REST:API}
OML Services
Import
model
OCI Data Science
Oracle APEX
Enterprise
Applications
Model deployment scenarios

Performs text analysis at scale
Understand unstructured text in documents e.g.:
• Customer feedback interactions
• Support tickets
• Social media
Built-in pre-trained models eliminates the need for machine learning expertise
Empowers developers to apply:
• Sentiment analysis
• Key-phrase extraction
• Text classification
• Named entity recognition
• + more
OCI Language

Provides automatic speech recognition
Real-time speech recognition using prebuilt models
Trained on thousands of native and non-native language speakers
Enables developers to easily:
• Convert file-based audio data containing human speech into highly
accurate text transcriptions
• Provide in-workflow closed captions
• Index content
• Enhance analytics on audio and video content
OCI Speech

Provides pre-trained computer vision models
Perform image recognition and document analysis tasks
Extend the models to other use cases e.g.:
• Scene monitoring
• Defect detection
• Document processing
Detect visual anomalies in manufacturing
Extract text from forms to automate business workflows
Tag items in images to count products or shipments
OCI Vision

Business-specific anomaly detection models
Flag critical irregularities early, which enables:
• Faster resolution
• Fewer operational disruption
Provides REST APIs and SDKs for several programming languages
Built on the patented MSET2 algorithm, which is used worldwide e.g.:
• Nuclear reactor health monitoring
• Fraud detection
• Predicting equipment breakdown
• Receiving data from multiple devices to predict failures
OCI Anomaly Detection

Delivers time-series forecasts
No need for data science expertise
Helps developers to quickly create accurate forecasts including:
• Product demand
• Revenue
• Resource requirements
Forecasts all have confidence intervals and explainability to help developers
make the right business decisions
OCI Forecasting

Helps users build labeled datasets to train AI models
Via user interfaces and public APIs, users can:
• Assemble data
• Create and browse datasets
• Apply labels to data records
The labeled data sets can be exported and used for model development
across many of Oracle’s AI and data science services
OCI Data Labeling

Products and Services in Context
39 Copyright © 2021, Oracle and/or its affiliates. All rights reserved.
• A complete and
comprehensive platform
for AI/ML
• Logical layers supporting
wide range of personas
• Services integrated with
data management
services for data science
/ machine learning
solution deployment

Helpful Links
40
ORACLE MACHINE LEARNING ON O.COM
https://www.oracle.com/machine-learning
OML TUTORIALS
OML LiveLab: https://apexapps.oracle.com/pls/apex/dbpm/r/livelabs/view-workshop?p180_id=560
OML4Py LiveLab: https://apexapps.oracle.com/pls/apex/dbpm/r/livelabs/view-workshop?wid=786
Interactive tour: https://docs.oracle.com/en/cloud/paas/autonomous-database/oml-tour
OML OFFICE HOURS
https://asktom.oracle.com/pls/apex/asktom.search?office=6801#sessionss
ORACLE ANALYTICS CLOUD
https://www.oracle.com/solutions/business-analytics/data-visualization/examples.html
OML4PY ORACLE AUTOML UI OML SERVICES
Oracle Machine Learning AutoML UI (2m video)
Oracle Machine Learning Demonstration (6m video)
OML AutoML UI Technical Brief
Blog: Introducing Oracle Machine Learning AutoML UI
Oracle Machine Learning Services (2m video)
OML Services Technical Brief
Oracle Machine Learning Services Documentation
Blog: Introducing Oracle Machine Learning Services
GitHub Repository with OML Services examples
OML4Py (2m video)
OML4Py Introduction (17m video)
OML4Py Technical Brief
OML4Py User’s Guide
Blog: Introducing OML4Py
GitHub Repository with Python notebooks

Machine Learning and AI at Oracle

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