Data meets AI - ATP Roadshow India

Copyright © 2019, Oracle and/or its affiliates. All rights reserved. | Confidential – Oracle Internal/Restricted/Highly Restricted
When Data meets AI
Autonomous Database Workshop - India
Sandesh Rao
VP AIOps , Autonomous Database
1

Copyright © 2019, Oracle and/or its affiliates. All rights reserved. |
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whoami
Real Application
Clusters - HA
DataGuard- DR
Machine
Learning- AIOps
Enterprise
Management
Product Support
Big Data
Operational
Management
Home
Automation Geek
and AI/ML guy
@sandeshr
https://www.linkedin.com/in/raosandesh/

Agenda
• Machine Learning and AI
• Algorithms, tools & technologies
• Oracle & Machine Learning Initiatives
• Conclusions

Copyright © 2019, Oracle and/or its affiliates. All rights reserved. | 5
Why Machine Learning and why now?
• Lots of Data generated as exhaust from systems
– Cloud , different formats and interfaces , frameworks
• Machine Learning has become accessible
– Anyone can be a Data Scientist
– Algorithms are accessible as libraries aka scikit , keras ,
tensorflow , Oracle Analytics..
– Sandbox to get started as easy as a docker init
• Business use cases
• How to find value from the data , fewer guesses to make decisions

Academia
• Pursue PHD’s and write papers
sharing their findings
• Find statistical relevance
• Define algorithms
• Exploring new developments in
this space
• Present in NIST , ICML

Model Builders
• Take data and cleanup
• Build models
• Perform A/B testing to find which
works best for their requirements
• Deploy the new models
• Constant Hyperparameter tuning
Model Building
vs vs
67% 83% 72%

Model Consumers
• Applied machine learning
• Don’t know about the algorithms or the models
• Just consume them
• Use the models as an inference engine
• Apply patches to consume the latest models

Machine Learning uses in our Daily lives
Video Surveillance
Airport Security
Recommendations
Chat Bots
Financial Trading
Targeted Advertising
Search Results
Healthcare
Virtual Assistants
Fraud Detection
Personal Marketing
Route Prediction
Data Security
Self Driving Cars
Email Classification
All logos are copyrighted by the respective companies

ML Project Workflow
• Set Business Objectives
• Gather , Prepare and Cleanse Data
• Model Data
– Feature Extraction , Test , Train ,
Optimizer
– Loss Function , effectiveness
– Framework and Library to use
• Apply the Model as an inference
engine
– Decision making using the Model’s
output
– Tune Model till outcome is closer to
Business Objective
10
Set Business
Objectives
Understand Use
case
Create Pseudo
Code
Synthetic Data
Generation
Pick Tools and
Frameworks
Train Test Model
Deploy Model
Measure Results
and Feedback

https://www.slideshare.net/awahid/big-data-and-machine-learning-for-businesses

• Hierarchical k-means, Orthogonal
Partitioning Clustering, Expectation-
Maximization
Clustering
Feature Extraction/Attribute
Importance / Component Analysis
• Decision Tree, Naive Bayes, Random
Forest, Logistic Regression, Support
Vector Machine
Classification
12
Machine Learning Algorithms
• Multiple Regression, Support Vector
Machine, Linear Model, LASSO, Random
Forest, Ridge Regression, Generalized
Linear Model, Stepwise Linear Regression
Regression
Association & Collaborative Filtering
Reinforcement Learning - brute force,
Monte Carlo, temporal difference....
• Many different use cases
Neural network & deep Learning with
Deep Neural Network

Modeling Phase – AutoML to the rescue
Provide Dataset to
AutoML2
Configuration parameters
for model picked
Dataset is divided into
training set & testing set
Actual Training
Evaluate performance of
trained model
Tweak model parameters,
change predictors change
test/train data splits and
change algorithms
Pick model plus
parameters depending on
outcome and measure , F1
, Precision , Recall , MSE
Document all runs and
apply A/B testing to see
what the variations
produce
13

Tools and Services Assisting ML projects
DWCS

What is Oracle Doing Around Machine Learning?
• Big Data Appliance
• Big Data Discovery , Big Data Preparation Data Visualization Cloud
• Analytics Cloud
– Sales, Marketing, HCM on top of SaaS
• DaaS – Oracle Data Cloud , Eloqua ..
• Oracle Labs (labs.oracle.com)
– Machine Learning Research Group
• Autonomous Database
– Zeppelin Notebooks preloaded with use cases
– Applied Machine Learning used for Implementing AIOps
15

Oracle AI Platform Cloud Service – Coming Soon…
• Collaborative end-to-end machine learning in the cloud
• Enables data science teams to
– Organize their work
– Access data and computing resources
– Build , Train , Deploy
– Manage models
• Collaborative , Self-Service , Integrated
• https://cloud.oracle.com/en_US/ai-platform
16

Oracle Autonomous Data Warehouse Cloud Key Features
Highly Elastic
Independently scale compute and
storage, without having to overpay for
fixed blocks of resources
Built-in Web-Based SQL ML Tool
Apache Zeppelin Oracle Machine Learning
notebooks ready to run ML from browser
Database migration utility
Dedicated cloud-ready migration tools
for easy migration from Amazon
Redshift, SQL Server and other databases
Enterprise Grade Security
Data is encrypted by default in the cloud,
as well as in transit and at rest
High-Performance Queries
and Concurrent Workloads
Optimized query performance with
preconfigured resource profiles for different
types of users
Oracle SQL
Autonomous DW Cloud is compatible with
all business analytics tools that support
Oracle Database
Self Driving
Fully automated database for self-tuning
patching and upgrading itself while the
system is running
Cloud-Based Data Loading
Fast, scalable data-loading from Oracle
Object Store, AWS S3, or on-premises
17
Oracle Machine Learning

Oracle Machine Learning and Advanced Analytics
• Support multiple data platforms, analytical engines, languages, UIs and
deployment strategies
Strategy and Road Map
Big Data / Big Data Cloud Relational
ML Algorithms
Common core, parallel, distributed
SQL R, Python, etc.GUI
Data Miner, RStudio
Notebooks
Advanced Analytics
Oracle Database Cloud DWCS

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
Machine Learning Notebook for Autonomous Data Warehouse Cloud

Oracle Machine Learning UI in ADW

Oracle Machine Learning for Python
Oracle Advanced Analytics option to Oracle Database >= 18c
• Use Oracle Database as HPC environment
• Use in-database parallel and distributed
machine learning algorithms
• Manage Python scripts and
Python objects in Oracle Database
• Integrate Python results into applications
and dashboards via SQL
• Produce better models faster with
automated machine learning
23
Oracle Database
User tables
In-db
stats
Database
Server
Machine
SQL Interfaces
SQL*Plus,
SQLDeveloper, …
for Python
Python Client

AutoML – new with OML4Py in Oracle Advanced Analytics
• Goal: increase model quality and data scientist productivity while reducing overall
compute time
• Auto Feature Selection
– Reduce the number of features by identifying most relevant
– Improve performance and accuracy
• Auto Model Selection for classification and regression
– Identify best algorithm to achieve maximum score
– Find best model many times faster than with exhaustive search techniques
• Auto Tuning of Hyper-parameters
– Significantly improve model accuracy
– Avoid manual or exhaustive search techniques
24

PaaS Resource Lifecycle
Management
Bare-Metal thru Installation
Upgrade
Patching
Dependency Resolution
Prerequisites Resolution
Required Capabilities
Automatable
Scalable
Online (if possible)
PaaS Application Lifecycle
Management
Installation
Upgrade
Patching
Dependency Resolution
Prerequisites Resolution
Workload Profile Identification
Placement determination
SLA management
Automatable
Provider Interoperable
Cloud Operations Early Warning
Response System
Detect degradations and faults
Pinpoint root cause & component
Push warnings and alerts
Push targeted corrective actions
SLA – based resource management
Real-time Health Dashboard
Continuous and frequent
Autonomous Action Enabled
OSS Integration Enabled
Management Interoperable
AIOps Cloud Operations
Resource Lifecycle Management Database Lifecycle Management Database Autonomous Self-Repair

Applied Machine Learning for Cloud Operations
• Generic ML-extracted Data Clusters
are insufficient for diagnostics
• Operational data correlation does
not determine root cause
• Trusted root cause determination
critical to swift corrective actions
• Algorithms selected and models
built require domain expertise
• Models refined via field feedback
Subject Matter
ExpertLog
ASH
Metrics
ML
Knowledge
Extraction
Model
Generation
Human
Supervision
Application
Optimized
Models
Feedback
Scrub Data

Autonomous Database
Cloud Platform
MachinesSmart Collectors
SRs
Expert Input
Feedback &
Improvement
Bugs
1
SRs
Logs
Model
Generation
Model
Knowledge
Extraction
Applied Machine Learning
Cloud Ops
Object Store
Admin UI in Control Plane
Oracle Support
Bug DB
SE UI in Support
Tenant (CNS)
Cleansing,
metadata creation
& clustering
5 Model generation
with expert scrubbing
6
Deployed as
part of cloud
image, running
from the start
1 Proactive regular health checking, real-
time fault detection, automatic
incident analysis, diagnostic collection
& masking of sensitive data
2
Use real-time health dashboards for anomaly
detection, root cause analysis & push of
proactive, preventative & corrective actions.
Auto bug search & auto bug & SR creation.
3
Auto SR analysis, diagnosis assistance via
automatic anomaly detection, collaboration
and one click bug creation
4
Message
Broker

Oracle Machine Learning Use Cases
Remove clutter from
log files to find the
most important events
to enable root cause
analysis
Preserve instance
performance when
database resources
are constrained
Find next best window
when maintenance
can be performed with
minimal service
impact
Discover duplicate
bugs, correlated issues
and prioritize based
upon customer impact
Identify a series of
events as connected
and representing the
signature of a problem
Identify the best
indexes

Remove clutter from log files to find the most important
events to enable root cause analysis
Log
Cleansing
1 2 3 4 5 6
Entry Feature
Creation
Entry
Clustering
Model
Generation
Expert
Input
Knowledge Base
Creation
Knowledge
Base Indexing
Feedback
Training
Real-time
Log File Processing
Timestamp Correlation & Ranking
8 9
7
Batch
Feedback

Preserve instance performance when database resources
are constrained
Reads 150 OS and DB
Performance data directly from
memory
Uses Bayesian Network-based
diagnostic root-cause models
•Models and remembers
normal operational data
points
Detects common RAC database
problems
•Determines if data is valid
•Is behavior expected
•Is there a problem
•What is causing the problem
•Is a failure likely
Performs root cause analysis
•Uses diagnostic inference engine to match symptoms to
route cause
•Used cross node and cross instance diagnostic Inference
Sends alerts and preventative actions to
Cloud Ops per target
1 2 3
4 5

START_TIME CNT
2018-04-11 15:00:00 290
2018-04-11 16:00:00 31120
2018-04-11 17:00:00 21530
2018-04-11 18:00:00 26240
2018-04-11 19:00:00 40520
2018-04-11 20:00:00 54270
2018-04-11 21:00:00 51460
2018-04-11 22:00:00 44310
2018-04-11 23:00:00 25690
START_TIME
2018-04-11 15:00:00 -0.226098
2018-04-11 16:00:00 -0.069821
2018-04-11 17:00:00 -0.350088
2018-04-11 18:00:00 -0.187483
2018-04-11 19:00:00 -0.513240
2018-04-11 20:00:00 0.019737
2018-04-11 21:00:00 0.059213
2018-04-11 22:00:00 -0.011312
2018-04-11 23:00:00 -0.179156
START_TIME
2018-04-11 15:00:00 5.669881
2018-04-11 16:00:00 10.345606
2018-04-11 17:00:00 9.977203
2018-04-11 18:00:00 10.175040
2018-04-11 19:00:00 10.609551
2018-04-11 20:00:00 10.901727
2018-04-11 21:00:00 10.848560
2018-04-11 22:00:00 10.698966
2018-04-11 23:00:00 10.153857
Current Date : 2018-05-12 15:00:00
Current Position in Seasonality : -0.22609829742533585
Best Maintenance Period in next Cycle : 2018-05-12 19:00:00
Worst Maintenance Period in next Cycle : 2018-05-13 08:00:00
Original observation data1 Apply convolution filter & average2 Calculate seasonality3
Use seasonality to
predict best
maintenance window
4
Find next best window when maintenance can be
performed with minimal service impact

BUG
DB
Discover duplicate bugs, correlated issues and prioritize
based upon customer impact
• Bugs are submitted from over 400 Oracle
products
• Performs ML Logistic Regression on training
set of bugs to generate model
• Displays up to 8 possible duplicates per bug
or SR
• Feedback improves model accuracy
– Direct from developers
– Indirect from bug updates
ABS Dev TeamBugs
Bugs
DupBugs
ML Logistic
Regression
Model
Generation
Expert
Supervision
ABS
Runtime
Model
Dev
Feedback
Bug
Submission Bug and
Duplicates
Together
ABS
Service
Feedback
Scrub Data
TFA
Customer SRDCs
(Service Request
Diagnostic Collection)

Identify a series of events as connected and representing
the signature of a problem
3. Identify anomalous entries and lifecycle events in
chronological order within a predefined time window around
the occurrence of the problem in all the logs
4. Compare the repeating anomalous / lifecycle entries to
identify the longest common subsequence of anomalous
entries
1. Start by classifying a problem
such as an important ORA or CRS
error
2. Find occurrences of the problem
across many different log files

• An expert system that implements
indexes based on what a
performance engineer skilled in index
tuning would do
• It identifies candidate indexes and
validates them before implementing
• The entire process is full automatic
• Transparency is equally important as
sophisticated automation
– All tuning activities are auditable via
reporting
34
Identify the best indexes
Capture
Identify
VerifyDecide
Monitor

Conclusions
• ML is here to stay and is just getting started
• The last 2.5 years of advances in this field dwarfs the previous 50 years of
growth
• We need to identify use cases to make the business better
• Modeling and ML infrastructure will become standard aka AutoML
• Getting the right data to train matters to have a successful outcome
• Models will get better with sparse data
• Most enterprise applications are already using embedded ML

https://ogyatra.in/cfp/
OGYatra 2019 - Dates

AIOUG Annual Conference
6-7 Dec 2019, HICC, Hyderabad

Data meets AI - ATP Roadshow India

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