SlideShare uma empresa Scribd logo
1 de 61
Baixar para ler offline
VP AIOps for the Autonomous Database
Sandesh Rao
Sangam AIOUG
Introduction to Machine Learning and Data
Science using the Oracle Autonomous Database
@sandeshr
https://www.linkedin.com/in/raosandesh/
https://www.slideshare.net/SandeshRao4
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, timing, and pricing of any features or functionality described for Oracle’s
products may change and remains at the sole discretion of Oracle Corporation.
Statements in this presentation relating to Oracle’s future plans, expectations, beliefs, intentions and
prospects are “forward-looking statements” and are subject to material risks and uncertainties. A
detailed discussion of these factors and other risks that affect our business is contained in Oracle’s
Securities and Exchange Commission (SEC) filings, including our most recent reports on Form 10-K and
Form 10-Q under the heading “Risk Factors.” These filings are available on the SEC’s website or on
Oracle’s website at http://www.oracle.com/investor. All information in this presentation is current as of
September 2019 and Oracle undertakes no duty to update any statement in light of new information or
future events.
Safe harbor statement
1. Overview of ML and the Autonomous Database
2. Regression
3. Classification
4. Clustering
5. Anomaly detection
6. Workload prediction
7. Dynamic maintenance windows
8. Oracle Machine Learning examples
Agenda
Overview of ML and
Autonomous Database
Tasks Specific to Business and Innovation
• Architecture, planning, data modeling
• Data security and lifecycle management
• Application related tuning
• End-to-End service level management
Maintenance Tasks
• Configuration and tuning of systems, network, storage
• Database provisioning, patching
• Database backups, H/A, disaster recovery
• Database optimization
Traditionally DBAs are Responsible for:
Value Scale
Innovation
Maintenance
Tasks Specific to Business and Innovation
• Architecture, planning, data modeling
• Data security and lifecycle management
• Application related tuning
• End-to-End service level management
Maintenance Tasks
• Configuration and tuning of systems, network, storage
• Database provisioning, patching
• Database backups, H/A, disaster recovery
• Database optimization
Freedom from Drudgery for DBA: More Time to Innovate and Improve the Business
Autonomous Database Removes Generic Tasks
Value Scale
Innovation
Maintenance
Machine Learning
Solving data-driven
problems
Discovering insights
Making predictions
Data Security
Data classification,
Data life-cycle mgmt
Application Tuning
SQL tuning,
connection mgmt
The Evolution of the DBA/Database Developer Role
Data Engineer
Architecture,
“data wrangler”
Data extraction
Data wrangling
Deriving new attributes
(“feature engineering”)
…
…
…
Import predictions & insights
Translate and deploy ML models
Automate
You Are Probably Already Doing Most of This Work!
Database Developer to Data Scientist Journey
1 - https://www.infoworld.com/article/3228245/data-science/the-80-20-data-science-dilemma.html
Typically 80% of the work
Most data scientists spend only 20 percent of their time
on actual data analysis and 80 percent of their time
finding, cleaning, and reorganizing huge amounts of
data, which is an inefficient data strategy1
Eliminated or minimized with Oracle
Data Management platform becomes
combine/hybrid DM + machine learning platform
Albert Einstein
“If I had an hour to solve a
problem I'd spend 55
minutes thinking about the
problem and 5 minutes
thinking about solutions.”
Lots of Data needs to be crunched
• No time to manually sift through the data
Machine Learning has become accessible
• Anyone can be a Data Scientist
• Software and algorithms are available
• Frameworks allow for massive training with no coding
• CI/CD available for MLOps
Business use cases
- How to find value from the data
Why Machine Learning for us and why now?
Analytics Value vs. Maturity
Reports &
Dashboards
Data
Information
Predictions & Insights Appls with ML
Analytical Maturity
ValueofAnalytics
Diagnostic
Analysis &
Reports
Predictive /
Machine
Learning
“ML Enabled”
Applications
What Happened?
Why it Happened?
What WILL happen?
Automated ML Appls
Database Developer to Data Scientist Journey
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
For the algorithm to function efficiently, it is
important to pick the right value for hyper parameters
(algorithm input parameters to the algorithm)
Once the training data in
the algorithm are
combined we get a model
1
2
3
4
5
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
Regression
REGRESSION
Predicting numbers
Customer lifetime value
Estimate optimal pricing
House price estimates
-
10
0 10 20 30 40 50 60
Classification
CLASSIFICATION
Membership of a
known class
Identify likely high
value customers
Find customer
likely to churn
Fraud detection
Clustering
CLUSTERING
Membership of an
inferred class
Customer segmentation
Credit risk evaluation
Document similarity
Anomaly Detection
ANOMALY
DETECTION
Outliers
Dentist billing
85 fillings / hour
Employees with high
claims / grade
One variable moving
out of sync
ASSOCIATION
RULES
Finding like-
minded people
You might be
interested in...
Root cause analysis
Identify “harbingers
of failure”
TIME SERIES
Temporal Aspect
Hitting a threshold
Forecasting energy use
Seasonality of data
GRAPH
ANALYTICS
Supplement ML
algorithm
Customer churn
Network outage
Fraud detection
NEURAL
NETWORKS
Learn (More) Like A
Human
Classification
Regression
Deep learning
http://neuralnetworksanddeeplearning.com/chap1.html
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
• 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
ML To determine workload
and deviation from it
What is Workload
Automatically
check
workload for
past x mins
Decide if
workload is
abnormally
high
Highlight any
abnormal
workload
issues
Optionally run on
demand
Optionally snooze
checking of a
component
Calculated via machine learning
Adaptive Learning
Workload Process
Captures metrics for key
performance dimensions across 5 X
1 minute time windows
CAPTURE1
Using semi-supervised learning via
SME threshold rules, the following
models are retrained :
• Isolation Forest
• One-Class Support Vector Machine
• Local Outlier Factor
Model with highest confidence
becomes the primary, if confidence is
high enough
TRAIN2
Straight after capture, the primary model
is used to predict anomalies.
Where anomalies are identified, metrics
are compared to SME threshold rules to
identify the type of anomaly
PREDICT3
Every
5 Mins
Every
Week
Every
5 Mins
Prediction (Every 5 minutes)
5 X 1 min metrics captured for
each dimension & ASH report
captured for later analysis
Metrics evaluated by the primary model to
determine if there are anomalies
If there is no primary model
(i.e. <7 days of data or <=95% model confidence)
then SME rules are used for anomaly detection
Each anomaly is compared against
the SME rules to determine which
dimension it applies to
Any anomalies are raised
along with recently
captured ASH report
Resource usage prediction
Configurable threshold
boundary – notify Admin of
forecasts above here
Actual values
(Black)
Forecast values
(Blue line)
Upper & lower
forecast range
(light blue area)
Unusual values
(anomalies)
Future forecast
values
ML To determine dynamic
maintenance windows
Identify Relevant Workload Metrics
• Ex: Average Active Sessions, CPU/Mem/IO Utilization
Time Series Decomposition
• Trend
• Seasonality
• Residual
Workload Seasonality Determination Locating Minimas
Optimum Window Identification and Validation
Model Generation and Training Flow
Maintenance Slot Identification
Maintenance window identification
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 data
1
Convolution filter & average
2
Calculate seasonality
3
Use seasonality to
predict best
maintenance window
4
OML examples
Simple SQL Syntax—Statistical Comparisons (t-tests)
Compare AVE Purchase Amounts Men vs. Women Grouped_By INCOME_LEVEL
Statistical Functions
SELECT SUBSTR(cust_income_level, 1, 22) income_level,
AVG(DECODE(cust_gender, 'M', amount_sold, null)) sold_to_men,
AVG(DECODE(cust_gender, 'F', amount_sold, null)) sold_to_women,
STATS_T_TEST_INDEPU(cust_gender, amount_sold, 'STATISTIC', 'F') t_observed,
STATS_T_TEST_INDEPU(cust_gender, amount_sold) two_sided_p_value
FROM customers c, sales s
WHERE c.cust_id = s.cust_id
GROUP BY ROLLUP(cust_income_level)
ORDER BY income_level, sold_to_men, sold_to_women, t_observed;
STATS_T_TEST_INDEPU (SQL) Example;
P_Values < 05 show statistically
significantly differences in the amounts
purchased by men vs. women
Simple SQL Syntax—Attribute Importance - ML Model Build (PL/SQL)
OAA Model Build and Real-time SQL Apply Prediction
BEGIN
DBMS_DATA_MINING.CREATE_MODEL(
model_name => 'BUY_INSURANCE_AI',
mining_function => DBMS_DATA_MINING.ATTRIBUTE_IMPORTANCE,
data_table_name => 'CUST_INSUR_LTV',
case_id_column_name => 'cust_id',
target_column_name => 'BUY_INSURANCE',
settings_table_name => 'Att_Import_Mode_Settings');
END;
/
SELECT attribute_name, rank , attribute_value
FROM BUY_INSURANCE_AI
ORDER BY rank, attribute_name;
Model Results (SQL query)
ATTRIBUTE_NAME RANK ATTRIBUTE_VALUE
BANK_FUNDS 1 0.2161
MONEY_MONTLY_OVERDRAWN 2 0.1489
N_TRANS_ATM 3 0.1463
N_TRANS_TELLER 4 0.1156
T_AMOUNT_AUTOM_PAYMENTS 5 0.1095
A1A2A3A4 A5A6 A7
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
Multiple Languages UIs Supported for End Users & Apps Development
Oracle Machine Leaning
Application DevelopersDBAs
R & Python Data Scientists “Citizen” Data ScientistsNotebook Users & DS Teams
New! New!
Target “best” customers who have GOOD CREDIT and make payments
Business Usecase
Define Problem Statement
Poorly Defined Better
Data Mining
Technique
Predict employees that leave
• Based on past employees that voluntarily left:
• Create New Attribute EmplTurnover à O/1
Predict customers that churn
• Based on past customers that have churned:
• Create New Attribute Churn à YES/NO
Target “best” customers
• Recency, Frequency Monetary (RFM) Analysis
• Specific Dollar Amount over Time Window:
• Who has spent $500+ in most recent 18 months
How can I make more $$? • What helps me sell soft drinks & coffee?
Which customers are likely to buy? • How much is each customer likely to spend?
Who are my “best customers”? • What descriptive “rules” describe “best customers”?
How can I combat fraud?
• Which transactions are the most anomalous?
• Then roll-up to physician, claimant, employee…
Data loading and Review
Target “best” customers who have GOOD CREDIT and make payments
Data Scoping
Create New Derived Attributes or “Engineered Features”
Feature Engineering
Source Attribute New Attribute/”Engineered Feature”
Date of Birth AGE
Address DISTANCE_TO_DESTINATION
COMMUTE_TIME
Call detail records (CDRs) #_DROPPED_CALLS
PERCENT_iNTERNATIONAL
Salary PERCENT_VS_PEERS
Purchases TOTALS_PER_CATEGORY (e.g. Food,
Clothing)
Create new derived attributes to tease more
information out of the data. For example:
• RECENCY, FREQUENCY, MONETARY
(RFM Analysis)
Create New Derived Attributes or “Engineered Features”
Feature Engineering
Data remains in Database
• Model building and scoring occur in-
database
• Leverage investment in Oracle IT
• Eliminate data duplication
- Eliminate separate analytical servers
Deliver enterprise-wide
“predictive” applications
Don’t move the Data
Traditional ML
Hours, Days or Weeks
Data Extraction
Data Prep &
Transformation
Data Mining
Model Building
Data Mining
Model “Scoring”
Data Prep. &
Transformation
Data Import
avings
Model “Scoring”
Embedded Data Prep
Data Preparation
Model Building
Oracle’s in-DB Machine Learning
Secs, Mins or Hours
ORACLE
AUTONOMOUS
DATABASE
Increasing sources of relevant data can
boost model accuracy
More Data Variety—Better Predictive Models
Model with 20 variables
Model with “Big Data” and
hundreds -- thousands of
input variables including:
• Demographic data
• Purchase POS transactional
data
• “Unstructured data”, text &
comments
• Spatial location data
• Long term vs. recent
historical behavior
• Web visits
• Sensor data
• etc.
Naïve Guess
or Random
100%
0% Population Size
Responders
Model with 75
variables
Model with 250
variables
100%
Engineered Features – Derived attributes/variable
that reflect domain knowledge—key to best models
First, Identify the Key Attributes That Most Influence the Target Attribute
Modeling and Machine Learning
Attribute Importance Model
Next, Build Predictive Models to Predict Customers who are Likely to Have Good_Credit
Modeling and Machine Learning
Split Data into Train and Test
Build and Test Classification Model
Test the ML model’s accuracy
• Randomly selected “hold out” sample
of data that was used to train the ML
model
• Compute Cumulative Gains, Lift,
Accuracy, etc.
• Review the attributes used in the model
and model coefficients
• Make sure the model makes sense
Next, Build Predictive Models to Predict Customers who are Likely to Have Good_Credit
Model Evaluation (Machine Learning)
Model Evaluation
Simple SQL Apply scripts run 100% inside the
Database for immediate ML model
deployment
Apply the Models to Predict “Best Customers”
Deployment
Model Apply/”Scoring”
Introduction to Machine Learning and Data Science using the Autonomous database - Sangam 2019
Congratulations!
Almost there J
Data Scientist
Manage and Analyze All Your Data
Big Data SQL / R
SQL / R / Python
Object
Store
“Engineered Features”
– Derived attributes
that reflect domain
knowledge—key to
best models e.g.:
• Counts
• Totals
• Changes
over time
Boil down the Data Lake
Architecturally,
lots of options
and flexibility
In-Database Machine Learning
More Models
Better Models
Faster, More Secure
Less Cost
Ready to Deploy!
No Need To Extract and
Move Data
Data stays in Database
Zero time required.
No production impact.
Data Preparation and
Transformation
Accelerated with
Automatic Data Prep
No separate environment
required. Much faster data prep.
Data stays protected and secured.
Data Mining and
Model Building
SQL, R, Python
Oracle Data Miner UI
OML Notebooks
Oracle Data Miner and AutoML
greatly speed model building.
Less skill required. No coding.
No Need to Transform
Production Data
Embedded Data
Preparation
No need for second
production instance.
Model Scoring
Accelerated Via
Exadata Database Machine
Faster model validation
Easy to repeat model building as often as needed
• OAA (Oracle Data Mining + Oracle R Enterprise) and ORAAH combined
• OAA includes support for Partitioned Models, Transactional, Unstructured, Geo-spatial, Graph data. etc,
Oracle’s Machine Learning & Adv. Analytics Algorithms
CLASSIFICATION
• Naïve Bayes
• Logistic Regression (GLM)
• Decision Tree
• Random Forest
• Neural Network
• Support Vector Machine
• Explicit Semantic Analysis
CLUSTERING
• Hierarchical K-Means
• Hierarchical O-Cluster
• Expectation Maximization (EM)
ANOMALY DETECTION
• One-Class SVM
TIME SERIES
• State of the art forecasting using
Exponential Smoothing
• Includes all popular models
e.g. Holt-Winters with trends,
seasons, irregularity, missing data
REGRESSION
• Linear Model
• Generalized Linear Model
• Support Vector Machine (SVM)
• Stepwise Linear regression
• Neural Network
• LASSO *
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
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 PACKAGES
• CRAN R Algorithm Packages
through Embedded R Execution
• Spark MLlib algorithm integration
EXPORTABLE ML MODELS
• REST APIs for deployment
X
1
X
2
A
1
A
2
A
3
A
4
A
5
A
6
A
7
ANALYTICAL SQL
• SQL Windows
• SQL Aggregate functions
• LAG/LEAD functions
• SQL for Pattern Matching
• Additional approximate
query
processing: APPROX_COUNT
, APPROX_SUM,
APPROX_RANK
• Regular Expressions
• Linear regression
• ANOVA (Analysis of
variance)
• Test Distribution fit
(e.g. Normal distribution
test, Binomial test, Weibull
test, Uniform
test, Exponential
test, Poisson test, etc.)
• Statistical Aggregates (min,
max, mean, median, stdev,
mode, quantiles, plus x
sigma, minus x sigma, top n
outliers, bottom n outliers)
STATISTICAL FUNCTIONS
• Descriptive statistics
(e.g. median, stdev, mode, sum,
etc.)
• Hypothesis testing
(t-test, F-test, Kolmogorov-
Smirnov test, Mann Whitney
test, Wilcoxon Signed Ranks test
• Correlations analysis
(parametric and nonparametric
e.g.
Pearson’s test for
correlation, Spearman's rho
coefficient, Kendall's tau-b
correlation coefficient)
• Ranking functions
• Cross Tabulations with Chi-square
statistics|
Oracle’s Machine Learning & Adv. Analytics Algorithms
Algorithms Operate on Data
ML and AI are just “Algorithms”
Move the Algorithms; Not the Data!;
It Changes Everything!
Thank You
Any Questions ?
Sandesh Rao
VP AIOps for the Autonomous Database
@sandeshr
https://www.linkedin.com/in/raosandesh/
https://www.slideshare.net/SandeshRao4
Introduction to Machine Learning and Data Science using the Autonomous database - Sangam 2019

Mais conteúdo relacionado

Mais procurados

DATA MINING on WEKA
DATA MINING on WEKADATA MINING on WEKA
DATA MINING on WEKAsatyamkhatri
 
Data Science Full Course | Edureka
Data Science Full Course | EdurekaData Science Full Course | Edureka
Data Science Full Course | EdurekaEdureka!
 
Creating a Data validation and Testing Strategy
Creating a Data validation and Testing StrategyCreating a Data validation and Testing Strategy
Creating a Data validation and Testing StrategyRTTS
 
What is a Data Warehouse and How Do I Test It?
What is a Data Warehouse and How Do I Test It?What is a Data Warehouse and How Do I Test It?
What is a Data Warehouse and How Do I Test It?RTTS
 
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
 
ETL Testing Interview Questions and Answers
ETL Testing Interview Questions and AnswersETL Testing Interview Questions and Answers
ETL Testing Interview Questions and AnswersH2Kinfosys
 
Big Data and BI Tools - BI Reporting for Bay Area Startups User Group
Big Data and BI Tools - BI Reporting for Bay Area Startups User GroupBig Data and BI Tools - BI Reporting for Bay Area Startups User Group
Big Data and BI Tools - BI Reporting for Bay Area Startups User GroupScott Mitchell
 
Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Req...
Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Req...Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Req...
Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Req...RTTS
 
AUSOUG - Introducing New AI Ops Innovations in Oracle 19c Autonomous Health F...
AUSOUG - Introducing New AI Ops Innovations in Oracle 19c Autonomous Health F...AUSOUG - Introducing New AI Ops Innovations in Oracle 19c Autonomous Health F...
AUSOUG - Introducing New AI Ops Innovations in Oracle 19c Autonomous Health F...Sandesh Rao
 
QuerySurge - the automated Data Testing solution
QuerySurge - the automated Data Testing solutionQuerySurge - the automated Data Testing solution
QuerySurge - the automated Data Testing solutionRTTS
 
DevSecOps PLM L2 Playbook.pdf
DevSecOps PLM L2 Playbook.pdfDevSecOps PLM L2 Playbook.pdf
DevSecOps PLM L2 Playbook.pdfNotTelling5
 
Real-time Recommendations for Retail: Architecture, Algorithms, and Design
Real-time Recommendations for Retail: Architecture, Algorithms, and DesignReal-time Recommendations for Retail: Architecture, Algorithms, and Design
Real-time Recommendations for Retail: Architecture, Algorithms, and DesignJuliet Hougland
 
Testing Big Data: Automated Testing of Hadoop with QuerySurge
Testing Big Data: Automated  Testing of Hadoop with QuerySurgeTesting Big Data: Automated  Testing of Hadoop with QuerySurge
Testing Big Data: Automated Testing of Hadoop with QuerySurgeRTTS
 
ETL Testing Training Presentation
ETL Testing Training PresentationETL Testing Training Presentation
ETL Testing Training PresentationApurba Biswas
 
Data Verification In QA Department Final
Data Verification In QA Department FinalData Verification In QA Department Final
Data Verification In QA Department FinalWayne Yaddow
 
Data Warehouse Testing in the Pharmaceutical Industry
Data Warehouse Testing in the Pharmaceutical IndustryData Warehouse Testing in the Pharmaceutical Industry
Data Warehouse Testing in the Pharmaceutical IndustryRTTS
 
"How to document your decisions", Dmytro Ovcharenko
"How to document your decisions", Dmytro Ovcharenko "How to document your decisions", Dmytro Ovcharenko
"How to document your decisions", Dmytro Ovcharenko Fwdays
 
Getting started-oracle-analytics-cloud
Getting started-oracle-analytics-cloudGetting started-oracle-analytics-cloud
Getting started-oracle-analytics-cloudapnambiar
 
A data driven etl test framework sqlsat madison
A data driven etl test framework sqlsat madisonA data driven etl test framework sqlsat madison
A data driven etl test framework sqlsat madisonTerry Bunio
 

Mais procurados (20)

DATA MINING on WEKA
DATA MINING on WEKADATA MINING on WEKA
DATA MINING on WEKA
 
Data Science Full Course | Edureka
Data Science Full Course | EdurekaData Science Full Course | Edureka
Data Science Full Course | Edureka
 
Creating a Data validation and Testing Strategy
Creating a Data validation and Testing StrategyCreating a Data validation and Testing Strategy
Creating a Data validation and Testing Strategy
 
What is a Data Warehouse and How Do I Test It?
What is a Data Warehouse and How Do I Test It?What is a Data Warehouse and How Do I Test It?
What is a Data Warehouse and How Do I Test It?
 
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...
 
ETL Testing Interview Questions and Answers
ETL Testing Interview Questions and AnswersETL Testing Interview Questions and Answers
ETL Testing Interview Questions and Answers
 
Big Data and BI Tools - BI Reporting for Bay Area Startups User Group
Big Data and BI Tools - BI Reporting for Bay Area Startups User GroupBig Data and BI Tools - BI Reporting for Bay Area Startups User Group
Big Data and BI Tools - BI Reporting for Bay Area Startups User Group
 
ETL QA
ETL QAETL QA
ETL QA
 
Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Req...
Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Req...Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Req...
Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Req...
 
AUSOUG - Introducing New AI Ops Innovations in Oracle 19c Autonomous Health F...
AUSOUG - Introducing New AI Ops Innovations in Oracle 19c Autonomous Health F...AUSOUG - Introducing New AI Ops Innovations in Oracle 19c Autonomous Health F...
AUSOUG - Introducing New AI Ops Innovations in Oracle 19c Autonomous Health F...
 
QuerySurge - the automated Data Testing solution
QuerySurge - the automated Data Testing solutionQuerySurge - the automated Data Testing solution
QuerySurge - the automated Data Testing solution
 
DevSecOps PLM L2 Playbook.pdf
DevSecOps PLM L2 Playbook.pdfDevSecOps PLM L2 Playbook.pdf
DevSecOps PLM L2 Playbook.pdf
 
Real-time Recommendations for Retail: Architecture, Algorithms, and Design
Real-time Recommendations for Retail: Architecture, Algorithms, and DesignReal-time Recommendations for Retail: Architecture, Algorithms, and Design
Real-time Recommendations for Retail: Architecture, Algorithms, and Design
 
Testing Big Data: Automated Testing of Hadoop with QuerySurge
Testing Big Data: Automated  Testing of Hadoop with QuerySurgeTesting Big Data: Automated  Testing of Hadoop with QuerySurge
Testing Big Data: Automated Testing of Hadoop with QuerySurge
 
ETL Testing Training Presentation
ETL Testing Training PresentationETL Testing Training Presentation
ETL Testing Training Presentation
 
Data Verification In QA Department Final
Data Verification In QA Department FinalData Verification In QA Department Final
Data Verification In QA Department Final
 
Data Warehouse Testing in the Pharmaceutical Industry
Data Warehouse Testing in the Pharmaceutical IndustryData Warehouse Testing in the Pharmaceutical Industry
Data Warehouse Testing in the Pharmaceutical Industry
 
"How to document your decisions", Dmytro Ovcharenko
"How to document your decisions", Dmytro Ovcharenko "How to document your decisions", Dmytro Ovcharenko
"How to document your decisions", Dmytro Ovcharenko
 
Getting started-oracle-analytics-cloud
Getting started-oracle-analytics-cloudGetting started-oracle-analytics-cloud
Getting started-oracle-analytics-cloud
 
A data driven etl test framework sqlsat madison
A data driven etl test framework sqlsat madisonA data driven etl test framework sqlsat madison
A data driven etl test framework sqlsat madison
 

Semelhante a Introduction to Machine Learning and Data Science using the Autonomous database - Sangam 2019

Introduction to Machine Learning and Data Science using Autonomous Database ...
Introduction to Machine Learning and Data Science using Autonomous Database  ...Introduction to Machine Learning and Data Science using Autonomous Database  ...
Introduction to Machine Learning and Data Science using Autonomous Database ...Sandesh Rao
 
Machine Learning in Autonomous Data Warehouse
 Machine Learning in Autonomous Data Warehouse Machine Learning in Autonomous Data Warehouse
Machine Learning in Autonomous Data WarehouseSandesh Rao
 
Introduction to Machine learning - DBA's to data scientists - Oct 2020 - OGBEmea
Introduction to Machine learning - DBA's to data scientists - Oct 2020 - OGBEmeaIntroduction to Machine learning - DBA's to data scientists - Oct 2020 - OGBEmea
Introduction to Machine learning - DBA's to data scientists - Oct 2020 - OGBEmeaSandesh Rao
 
Introduction to Machine Learning - From DBA's to Data Scientists - OGBEMEA
Introduction to Machine Learning - From DBA's to Data Scientists - OGBEMEAIntroduction to Machine Learning - From DBA's to Data Scientists - OGBEMEA
Introduction to Machine Learning - From DBA's to Data Scientists - OGBEMEASandesh Rao
 
Introduction to AutoML and Data Science using the Oracle Autonomous Database ...
Introduction to AutoML and Data Science using the Oracle Autonomous Database ...Introduction to AutoML and Data Science using the Oracle Autonomous Database ...
Introduction to AutoML and Data Science using the Oracle Autonomous Database ...Sandesh Rao
 
Architecting the Framework for Compliance & Risk Management
Architecting the Framework for Compliance & Risk ManagementArchitecting the Framework for Compliance & Risk Management
Architecting the Framework for Compliance & Risk Managementjadams6
 
ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...
ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...
ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...BigML, Inc
 
Rwa optimizer benefits and features
Rwa optimizer benefits and featuresRwa optimizer benefits and features
Rwa optimizer benefits and featuresAsif Rajani
 
Data drift and machine learning
Data drift and machine learningData drift and machine learning
Data drift and machine learningSmita Agrawal
 
#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...
#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...
#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...Agile Testing Alliance
 
State of the Market - Data Quality in 2023
State of the Market - Data Quality in 2023State of the Market - Data Quality in 2023
State of the Market - Data Quality in 2023RTTS
 
Data drift and machine learning
Data drift and machine learningData drift and machine learning
Data drift and machine learningSmita Agrawal
 
Amazon SageMaker 內建機器學習演算法 (Level 400)
Amazon SageMaker 內建機器學習演算法 (Level 400)Amazon SageMaker 內建機器學習演算法 (Level 400)
Amazon SageMaker 內建機器學習演算法 (Level 400)Amazon Web Services
 
The Automation Firehose: Be Strategic & Tactical With Your Mobile & Web Testing
The Automation Firehose: Be Strategic & Tactical With Your Mobile & Web TestingThe Automation Firehose: Be Strategic & Tactical With Your Mobile & Web Testing
The Automation Firehose: Be Strategic & Tactical With Your Mobile & Web TestingPerfecto by Perforce
 
The Automation Firehose: Be Strategic and Tactical by Thomas Haver
The Automation Firehose: Be Strategic and Tactical by Thomas HaverThe Automation Firehose: Be Strategic and Tactical by Thomas Haver
The Automation Firehose: Be Strategic and Tactical by Thomas HaverQA or the Highway
 
Navigating HCM Compliance Through Managed Services Part 2
Navigating HCM Compliance Through Managed Services Part 2Navigating HCM Compliance Through Managed Services Part 2
Navigating HCM Compliance Through Managed Services Part 2Smart ERP Solutions, Inc.
 
Best Practices for Rating and Policy Administration System Replacement
Best Practices for Rating and Policy Administration System ReplacementBest Practices for Rating and Policy Administration System Replacement
Best Practices for Rating and Policy Administration System ReplacementEdgewater
 
CA Mainframe Resource Intelligence
CA Mainframe Resource IntelligenceCA Mainframe Resource Intelligence
CA Mainframe Resource IntelligenceCA Technologies
 

Semelhante a Introduction to Machine Learning and Data Science using the Autonomous database - Sangam 2019 (20)

Introduction to Machine Learning and Data Science using Autonomous Database ...
Introduction to Machine Learning and Data Science using Autonomous Database  ...Introduction to Machine Learning and Data Science using Autonomous Database  ...
Introduction to Machine Learning and Data Science using Autonomous Database ...
 
Machine Learning in Autonomous Data Warehouse
 Machine Learning in Autonomous Data Warehouse Machine Learning in Autonomous Data Warehouse
Machine Learning in Autonomous Data Warehouse
 
Introduction to Machine learning - DBA's to data scientists - Oct 2020 - OGBEmea
Introduction to Machine learning - DBA's to data scientists - Oct 2020 - OGBEmeaIntroduction to Machine learning - DBA's to data scientists - Oct 2020 - OGBEmea
Introduction to Machine learning - DBA's to data scientists - Oct 2020 - OGBEmea
 
Introduction to Machine Learning - From DBA's to Data Scientists - OGBEMEA
Introduction to Machine Learning - From DBA's to Data Scientists - OGBEMEAIntroduction to Machine Learning - From DBA's to Data Scientists - OGBEMEA
Introduction to Machine Learning - From DBA's to Data Scientists - OGBEMEA
 
Introduction to AutoML and Data Science using the Oracle Autonomous Database ...
Introduction to AutoML and Data Science using the Oracle Autonomous Database ...Introduction to AutoML and Data Science using the Oracle Autonomous Database ...
Introduction to AutoML and Data Science using the Oracle Autonomous Database ...
 
Architecting the Framework for Compliance & Risk Management
Architecting the Framework for Compliance & Risk ManagementArchitecting the Framework for Compliance & Risk Management
Architecting the Framework for Compliance & Risk Management
 
ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...
ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...
ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...
 
Rwa optimizer benefits and features
Rwa optimizer benefits and featuresRwa optimizer benefits and features
Rwa optimizer benefits and features
 
Data drift and machine learning
Data drift and machine learningData drift and machine learning
Data drift and machine learning
 
NZS-4555 - IT Analytics Keynote - IT Analytics for the Enterprise
NZS-4555 - IT Analytics Keynote - IT Analytics for the EnterpriseNZS-4555 - IT Analytics Keynote - IT Analytics for the Enterprise
NZS-4555 - IT Analytics Keynote - IT Analytics for the Enterprise
 
Claims
ClaimsClaims
Claims
 
#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...
#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...
#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...
 
State of the Market - Data Quality in 2023
State of the Market - Data Quality in 2023State of the Market - Data Quality in 2023
State of the Market - Data Quality in 2023
 
Data drift and machine learning
Data drift and machine learningData drift and machine learning
Data drift and machine learning
 
Amazon SageMaker 內建機器學習演算法 (Level 400)
Amazon SageMaker 內建機器學習演算法 (Level 400)Amazon SageMaker 內建機器學習演算法 (Level 400)
Amazon SageMaker 內建機器學習演算法 (Level 400)
 
The Automation Firehose: Be Strategic & Tactical With Your Mobile & Web Testing
The Automation Firehose: Be Strategic & Tactical With Your Mobile & Web TestingThe Automation Firehose: Be Strategic & Tactical With Your Mobile & Web Testing
The Automation Firehose: Be Strategic & Tactical With Your Mobile & Web Testing
 
The Automation Firehose: Be Strategic and Tactical by Thomas Haver
The Automation Firehose: Be Strategic and Tactical by Thomas HaverThe Automation Firehose: Be Strategic and Tactical by Thomas Haver
The Automation Firehose: Be Strategic and Tactical by Thomas Haver
 
Navigating HCM Compliance Through Managed Services Part 2
Navigating HCM Compliance Through Managed Services Part 2Navigating HCM Compliance Through Managed Services Part 2
Navigating HCM Compliance Through Managed Services Part 2
 
Best Practices for Rating and Policy Administration System Replacement
Best Practices for Rating and Policy Administration System ReplacementBest Practices for Rating and Policy Administration System Replacement
Best Practices for Rating and Policy Administration System Replacement
 
CA Mainframe Resource Intelligence
CA Mainframe Resource IntelligenceCA Mainframe Resource Intelligence
CA Mainframe Resource Intelligence
 

Mais de Sandesh Rao

Whats new in Autonomous Database in 2022
Whats new in Autonomous Database in 2022Whats new in Autonomous Database in 2022
Whats new in Autonomous Database in 2022Sandesh Rao
 
Oracle Database performance tuning using oratop
Oracle Database performance tuning using oratopOracle Database performance tuning using oratop
Oracle Database performance tuning using oratopSandesh Rao
 
Analysis of Database Issues using AHF and Machine Learning v2 - AOUG2022
Analysis of Database Issues using AHF and Machine Learning v2 -  AOUG2022Analysis of Database Issues using AHF and Machine Learning v2 -  AOUG2022
Analysis of Database Issues using AHF and Machine Learning v2 - AOUG2022Sandesh Rao
 
Analysis of Database Issues using AHF and Machine Learning v2 - SOUG
Analysis of Database Issues using AHF and Machine Learning v2 -  SOUGAnalysis of Database Issues using AHF and Machine Learning v2 -  SOUG
Analysis of Database Issues using AHF and Machine Learning v2 - SOUGSandesh Rao
 
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021Sandesh Rao
 
15 Troubleshooting tips and Tricks for Database 21c - KSAOUG
15 Troubleshooting tips and Tricks for Database 21c - KSAOUG15 Troubleshooting tips and Tricks for Database 21c - KSAOUG
15 Troubleshooting tips and Tricks for Database 21c - KSAOUGSandesh Rao
 
Machine Learning and AI at Oracle
Machine Learning and AI at OracleMachine Learning and AI at Oracle
Machine Learning and AI at OracleSandesh Rao
 
Top 20 FAQs on the Autonomous Database
Top 20 FAQs on the Autonomous DatabaseTop 20 FAQs on the Autonomous Database
Top 20 FAQs on the Autonomous DatabaseSandesh Rao
 
How to Use EXAchk Effectively to Manage Exadata Environments
How to Use EXAchk Effectively to Manage Exadata EnvironmentsHow to Use EXAchk Effectively to Manage Exadata Environments
How to Use EXAchk Effectively to Manage Exadata EnvironmentsSandesh Rao
 
15 Troubleshooting Tips and Tricks for database 21c - OGBEMEA KSAOUG
15 Troubleshooting Tips and Tricks for database 21c - OGBEMEA KSAOUG15 Troubleshooting Tips and Tricks for database 21c - OGBEMEA KSAOUG
15 Troubleshooting Tips and Tricks for database 21c - OGBEMEA KSAOUGSandesh Rao
 
TFA Collector - what can one do with it
TFA Collector - what can one do with it TFA Collector - what can one do with it
TFA Collector - what can one do with it Sandesh Rao
 
How to use Exachk effectively to manage Exadata environments OGBEmea
How to use Exachk effectively to manage Exadata environments OGBEmeaHow to use Exachk effectively to manage Exadata environments OGBEmea
How to use Exachk effectively to manage Exadata environments OGBEmeaSandesh Rao
 
Troubleshooting tips and tricks for Oracle Database Oct 2020
Troubleshooting tips and tricks for Oracle Database Oct 2020Troubleshooting tips and tricks for Oracle Database Oct 2020
Troubleshooting tips and tricks for Oracle Database Oct 2020Sandesh Rao
 
TFA, ORAchk and EXAchk 20.2 - What's new
TFA, ORAchk and EXAchk 20.2 - What's new TFA, ORAchk and EXAchk 20.2 - What's new
TFA, ORAchk and EXAchk 20.2 - What's new Sandesh Rao
 
Oracle Autonomous Health Service- For Protecting Your On-Premise Databases- F...
Oracle Autonomous Health Service- For Protecting Your On-Premise Databases- F...Oracle Autonomous Health Service- For Protecting Your On-Premise Databases- F...
Oracle Autonomous Health Service- For Protecting Your On-Premise Databases- F...Sandesh Rao
 
The Machine Learning behind the Autonomous Database ILOUG Feb 2020
The Machine Learning behind the Autonomous Database   ILOUG Feb 2020 The Machine Learning behind the Autonomous Database   ILOUG Feb 2020
The Machine Learning behind the Autonomous Database ILOUG Feb 2020 Sandesh Rao
 
Troubleshooting Tips and Tricks for Database 19c ILOUG Feb 2020
Troubleshooting Tips and Tricks for Database 19c   ILOUG Feb 2020Troubleshooting Tips and Tricks for Database 19c   ILOUG Feb 2020
Troubleshooting Tips and Tricks for Database 19c ILOUG Feb 2020Sandesh Rao
 
Troubleshooting Tips and Tricks for Database 19c - Sangam 2019
Troubleshooting Tips and Tricks for Database 19c - Sangam 2019Troubleshooting Tips and Tricks for Database 19c - Sangam 2019
Troubleshooting Tips and Tricks for Database 19c - Sangam 2019Sandesh Rao
 
20 Tips and Tricks with the Autonomous Database
20 Tips and Tricks with the Autonomous Database 20 Tips and Tricks with the Autonomous Database
20 Tips and Tricks with the Autonomous Database Sandesh Rao
 
The Machine Learning behind the Autonomous Database- EMEA Tour Oct 2019
The Machine Learning behind the Autonomous Database- EMEA Tour Oct 2019 The Machine Learning behind the Autonomous Database- EMEA Tour Oct 2019
The Machine Learning behind the Autonomous Database- EMEA Tour Oct 2019 Sandesh Rao
 

Mais de Sandesh Rao (20)

Whats new in Autonomous Database in 2022
Whats new in Autonomous Database in 2022Whats new in Autonomous Database in 2022
Whats new in Autonomous Database in 2022
 
Oracle Database performance tuning using oratop
Oracle Database performance tuning using oratopOracle Database performance tuning using oratop
Oracle Database performance tuning using oratop
 
Analysis of Database Issues using AHF and Machine Learning v2 - AOUG2022
Analysis of Database Issues using AHF and Machine Learning v2 -  AOUG2022Analysis of Database Issues using AHF and Machine Learning v2 -  AOUG2022
Analysis of Database Issues using AHF and Machine Learning v2 - AOUG2022
 
Analysis of Database Issues using AHF and Machine Learning v2 - SOUG
Analysis of Database Issues using AHF and Machine Learning v2 -  SOUGAnalysis of Database Issues using AHF and Machine Learning v2 -  SOUG
Analysis of Database Issues using AHF and Machine Learning v2 - SOUG
 
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
 
15 Troubleshooting tips and Tricks for Database 21c - KSAOUG
15 Troubleshooting tips and Tricks for Database 21c - KSAOUG15 Troubleshooting tips and Tricks for Database 21c - KSAOUG
15 Troubleshooting tips and Tricks for Database 21c - KSAOUG
 
Machine Learning and AI at Oracle
Machine Learning and AI at OracleMachine Learning and AI at Oracle
Machine Learning and AI at Oracle
 
Top 20 FAQs on the Autonomous Database
Top 20 FAQs on the Autonomous DatabaseTop 20 FAQs on the Autonomous Database
Top 20 FAQs on the Autonomous Database
 
How to Use EXAchk Effectively to Manage Exadata Environments
How to Use EXAchk Effectively to Manage Exadata EnvironmentsHow to Use EXAchk Effectively to Manage Exadata Environments
How to Use EXAchk Effectively to Manage Exadata Environments
 
15 Troubleshooting Tips and Tricks for database 21c - OGBEMEA KSAOUG
15 Troubleshooting Tips and Tricks for database 21c - OGBEMEA KSAOUG15 Troubleshooting Tips and Tricks for database 21c - OGBEMEA KSAOUG
15 Troubleshooting Tips and Tricks for database 21c - OGBEMEA KSAOUG
 
TFA Collector - what can one do with it
TFA Collector - what can one do with it TFA Collector - what can one do with it
TFA Collector - what can one do with it
 
How to use Exachk effectively to manage Exadata environments OGBEmea
How to use Exachk effectively to manage Exadata environments OGBEmeaHow to use Exachk effectively to manage Exadata environments OGBEmea
How to use Exachk effectively to manage Exadata environments OGBEmea
 
Troubleshooting tips and tricks for Oracle Database Oct 2020
Troubleshooting tips and tricks for Oracle Database Oct 2020Troubleshooting tips and tricks for Oracle Database Oct 2020
Troubleshooting tips and tricks for Oracle Database Oct 2020
 
TFA, ORAchk and EXAchk 20.2 - What's new
TFA, ORAchk and EXAchk 20.2 - What's new TFA, ORAchk and EXAchk 20.2 - What's new
TFA, ORAchk and EXAchk 20.2 - What's new
 
Oracle Autonomous Health Service- For Protecting Your On-Premise Databases- F...
Oracle Autonomous Health Service- For Protecting Your On-Premise Databases- F...Oracle Autonomous Health Service- For Protecting Your On-Premise Databases- F...
Oracle Autonomous Health Service- For Protecting Your On-Premise Databases- F...
 
The Machine Learning behind the Autonomous Database ILOUG Feb 2020
The Machine Learning behind the Autonomous Database   ILOUG Feb 2020 The Machine Learning behind the Autonomous Database   ILOUG Feb 2020
The Machine Learning behind the Autonomous Database ILOUG Feb 2020
 
Troubleshooting Tips and Tricks for Database 19c ILOUG Feb 2020
Troubleshooting Tips and Tricks for Database 19c   ILOUG Feb 2020Troubleshooting Tips and Tricks for Database 19c   ILOUG Feb 2020
Troubleshooting Tips and Tricks for Database 19c ILOUG Feb 2020
 
Troubleshooting Tips and Tricks for Database 19c - Sangam 2019
Troubleshooting Tips and Tricks for Database 19c - Sangam 2019Troubleshooting Tips and Tricks for Database 19c - Sangam 2019
Troubleshooting Tips and Tricks for Database 19c - Sangam 2019
 
20 Tips and Tricks with the Autonomous Database
20 Tips and Tricks with the Autonomous Database 20 Tips and Tricks with the Autonomous Database
20 Tips and Tricks with the Autonomous Database
 
The Machine Learning behind the Autonomous Database- EMEA Tour Oct 2019
The Machine Learning behind the Autonomous Database- EMEA Tour Oct 2019 The Machine Learning behind the Autonomous Database- EMEA Tour Oct 2019
The Machine Learning behind the Autonomous Database- EMEA Tour Oct 2019
 

Último

9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding Team9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding TeamAdam Moalla
 
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsIgniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsSafe Software
 
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfJamie (Taka) Wang
 
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostKubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostMatt Ray
 
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDEADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDELiveplex
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1DianaGray10
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarPrecisely
 
Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Brian Pichman
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopBachir Benyammi
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URLRuncy Oommen
 
Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Commit University
 
COMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a WebsiteCOMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a Websitedgelyza
 
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationUsing IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationIES VE
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesDavid Newbury
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxMatsuo Lab
 
Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Adtran
 
Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.YounusS2
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemAsko Soukka
 
UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7DianaGray10
 

Último (20)

9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding Team9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding Team
 
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsIgniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
 
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
 
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostKubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
 
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDEADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity Webinar
 
Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 Workshop
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URL
 
Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)
 
20150722 - AGV
20150722 - AGV20150722 - AGV
20150722 - AGV
 
COMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a WebsiteCOMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a Website
 
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationUsing IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond Ontologies
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptx
 
Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™
 
Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystem
 
UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7
 

Introduction to Machine Learning and Data Science using the Autonomous database - Sangam 2019

  • 1. VP AIOps for the Autonomous Database Sandesh Rao Sangam AIOUG Introduction to Machine Learning and Data Science using the Oracle Autonomous Database @sandeshr https://www.linkedin.com/in/raosandesh/ https://www.slideshare.net/SandeshRao4
  • 2. 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, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation. Statements in this presentation relating to Oracle’s future plans, expectations, beliefs, intentions and prospects are “forward-looking statements” and are subject to material risks and uncertainties. A detailed discussion of these factors and other risks that affect our business is contained in Oracle’s Securities and Exchange Commission (SEC) filings, including our most recent reports on Form 10-K and Form 10-Q under the heading “Risk Factors.” These filings are available on the SEC’s website or on Oracle’s website at http://www.oracle.com/investor. All information in this presentation is current as of September 2019 and Oracle undertakes no duty to update any statement in light of new information or future events. Safe harbor statement
  • 3. 1. Overview of ML and the Autonomous Database 2. Regression 3. Classification 4. Clustering 5. Anomaly detection 6. Workload prediction 7. Dynamic maintenance windows 8. Oracle Machine Learning examples Agenda
  • 4. Overview of ML and Autonomous Database
  • 5. Tasks Specific to Business and Innovation • Architecture, planning, data modeling • Data security and lifecycle management • Application related tuning • End-to-End service level management Maintenance Tasks • Configuration and tuning of systems, network, storage • Database provisioning, patching • Database backups, H/A, disaster recovery • Database optimization Traditionally DBAs are Responsible for: Value Scale Innovation Maintenance
  • 6. Tasks Specific to Business and Innovation • Architecture, planning, data modeling • Data security and lifecycle management • Application related tuning • End-to-End service level management Maintenance Tasks • Configuration and tuning of systems, network, storage • Database provisioning, patching • Database backups, H/A, disaster recovery • Database optimization Freedom from Drudgery for DBA: More Time to Innovate and Improve the Business Autonomous Database Removes Generic Tasks Value Scale Innovation Maintenance
  • 7. Machine Learning Solving data-driven problems Discovering insights Making predictions Data Security Data classification, Data life-cycle mgmt Application Tuning SQL tuning, connection mgmt The Evolution of the DBA/Database Developer Role Data Engineer Architecture, “data wrangler”
  • 8. Data extraction Data wrangling Deriving new attributes (“feature engineering”) … … … Import predictions & insights Translate and deploy ML models Automate You Are Probably Already Doing Most of This Work! Database Developer to Data Scientist Journey 1 - https://www.infoworld.com/article/3228245/data-science/the-80-20-data-science-dilemma.html Typically 80% of the work Most data scientists spend only 20 percent of their time on actual data analysis and 80 percent of their time finding, cleaning, and reorganizing huge amounts of data, which is an inefficient data strategy1 Eliminated or minimized with Oracle Data Management platform becomes combine/hybrid DM + machine learning platform
  • 9. Albert Einstein “If I had an hour to solve a problem I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.”
  • 10. Lots of Data needs to be crunched • No time to manually sift through the data Machine Learning has become accessible • Anyone can be a Data Scientist • Software and algorithms are available • Frameworks allow for massive training with no coding • CI/CD available for MLOps Business use cases - How to find value from the data Why Machine Learning for us and why now?
  • 11. Analytics Value vs. Maturity Reports & Dashboards Data Information Predictions & Insights Appls with ML Analytical Maturity ValueofAnalytics Diagnostic Analysis & Reports Predictive / Machine Learning “ML Enabled” Applications What Happened? Why it Happened? What WILL happen? Automated ML Appls
  • 12. Database Developer to Data Scientist Journey
  • 13. 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 For the algorithm to function efficiently, it is important to pick the right value for hyper parameters (algorithm input parameters to the algorithm) Once the training data in the algorithm are combined we get a model 1 2 3 4 5
  • 14. 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
  • 16. REGRESSION Predicting numbers Customer lifetime value Estimate optimal pricing House price estimates - 10 0 10 20 30 40 50 60
  • 18. CLASSIFICATION Membership of a known class Identify likely high value customers Find customer likely to churn Fraud detection
  • 20. CLUSTERING Membership of an inferred class Customer segmentation Credit risk evaluation Document similarity
  • 22. ANOMALY DETECTION Outliers Dentist billing 85 fillings / hour Employees with high claims / grade One variable moving out of sync
  • 23. ASSOCIATION RULES Finding like- minded people You might be interested in... Root cause analysis Identify “harbingers of failure”
  • 24. TIME SERIES Temporal Aspect Hitting a threshold Forecasting energy use Seasonality of data
  • 26. NEURAL NETWORKS Learn (More) Like A Human Classification Regression Deep learning http://neuralnetworksanddeeplearning.com/chap1.html
  • 27. 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 • 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
  • 28. ML To determine workload and deviation from it
  • 29. What is Workload Automatically check workload for past x mins Decide if workload is abnormally high Highlight any abnormal workload issues Optionally run on demand Optionally snooze checking of a component Calculated via machine learning
  • 30. Adaptive Learning Workload Process Captures metrics for key performance dimensions across 5 X 1 minute time windows CAPTURE1 Using semi-supervised learning via SME threshold rules, the following models are retrained : • Isolation Forest • One-Class Support Vector Machine • Local Outlier Factor Model with highest confidence becomes the primary, if confidence is high enough TRAIN2 Straight after capture, the primary model is used to predict anomalies. Where anomalies are identified, metrics are compared to SME threshold rules to identify the type of anomaly PREDICT3 Every 5 Mins Every Week Every 5 Mins
  • 31. Prediction (Every 5 minutes) 5 X 1 min metrics captured for each dimension & ASH report captured for later analysis Metrics evaluated by the primary model to determine if there are anomalies If there is no primary model (i.e. <7 days of data or <=95% model confidence) then SME rules are used for anomaly detection Each anomaly is compared against the SME rules to determine which dimension it applies to Any anomalies are raised along with recently captured ASH report
  • 32. Resource usage prediction Configurable threshold boundary – notify Admin of forecasts above here Actual values (Black) Forecast values (Blue line) Upper & lower forecast range (light blue area) Unusual values (anomalies) Future forecast values
  • 33. ML To determine dynamic maintenance windows
  • 34. Identify Relevant Workload Metrics • Ex: Average Active Sessions, CPU/Mem/IO Utilization Time Series Decomposition • Trend • Seasonality • Residual Workload Seasonality Determination Locating Minimas Optimum Window Identification and Validation Model Generation and Training Flow Maintenance Slot Identification
  • 35. Maintenance window identification 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 data 1 Convolution filter & average 2 Calculate seasonality 3 Use seasonality to predict best maintenance window 4
  • 37. Simple SQL Syntax—Statistical Comparisons (t-tests) Compare AVE Purchase Amounts Men vs. Women Grouped_By INCOME_LEVEL Statistical Functions SELECT SUBSTR(cust_income_level, 1, 22) income_level, AVG(DECODE(cust_gender, 'M', amount_sold, null)) sold_to_men, AVG(DECODE(cust_gender, 'F', amount_sold, null)) sold_to_women, STATS_T_TEST_INDEPU(cust_gender, amount_sold, 'STATISTIC', 'F') t_observed, STATS_T_TEST_INDEPU(cust_gender, amount_sold) two_sided_p_value FROM customers c, sales s WHERE c.cust_id = s.cust_id GROUP BY ROLLUP(cust_income_level) ORDER BY income_level, sold_to_men, sold_to_women, t_observed; STATS_T_TEST_INDEPU (SQL) Example; P_Values < 05 show statistically significantly differences in the amounts purchased by men vs. women
  • 38. Simple SQL Syntax—Attribute Importance - ML Model Build (PL/SQL) OAA Model Build and Real-time SQL Apply Prediction BEGIN DBMS_DATA_MINING.CREATE_MODEL( model_name => 'BUY_INSURANCE_AI', mining_function => DBMS_DATA_MINING.ATTRIBUTE_IMPORTANCE, data_table_name => 'CUST_INSUR_LTV', case_id_column_name => 'cust_id', target_column_name => 'BUY_INSURANCE', settings_table_name => 'Att_Import_Mode_Settings'); END; / SELECT attribute_name, rank , attribute_value FROM BUY_INSURANCE_AI ORDER BY rank, attribute_name; Model Results (SQL query) ATTRIBUTE_NAME RANK ATTRIBUTE_VALUE BANK_FUNDS 1 0.2161 MONEY_MONTLY_OVERDRAWN 2 0.1489 N_TRANS_ATM 3 0.1463 N_TRANS_TELLER 4 0.1156 T_AMOUNT_AUTOM_PAYMENTS 5 0.1095 A1A2A3A4 A5A6 A7
  • 39. 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
  • 40. Multiple Languages UIs Supported for End Users & Apps Development Oracle Machine Leaning Application DevelopersDBAs R & Python Data Scientists “Citizen” Data ScientistsNotebook Users & DS Teams New! New!
  • 41. Target “best” customers who have GOOD CREDIT and make payments Business Usecase
  • 42. Define Problem Statement Poorly Defined Better Data Mining Technique Predict employees that leave • Based on past employees that voluntarily left: • Create New Attribute EmplTurnover à O/1 Predict customers that churn • Based on past customers that have churned: • Create New Attribute Churn à YES/NO Target “best” customers • Recency, Frequency Monetary (RFM) Analysis • Specific Dollar Amount over Time Window: • Who has spent $500+ in most recent 18 months How can I make more $$? • What helps me sell soft drinks & coffee? Which customers are likely to buy? • How much is each customer likely to spend? Who are my “best customers”? • What descriptive “rules” describe “best customers”? How can I combat fraud? • Which transactions are the most anomalous? • Then roll-up to physician, claimant, employee…
  • 44. Target “best” customers who have GOOD CREDIT and make payments Data Scoping
  • 45. Create New Derived Attributes or “Engineered Features” Feature Engineering Source Attribute New Attribute/”Engineered Feature” Date of Birth AGE Address DISTANCE_TO_DESTINATION COMMUTE_TIME Call detail records (CDRs) #_DROPPED_CALLS PERCENT_iNTERNATIONAL Salary PERCENT_VS_PEERS Purchases TOTALS_PER_CATEGORY (e.g. Food, Clothing)
  • 46. Create new derived attributes to tease more information out of the data. For example: • RECENCY, FREQUENCY, MONETARY (RFM Analysis) Create New Derived Attributes or “Engineered Features” Feature Engineering
  • 47. Data remains in Database • Model building and scoring occur in- database • Leverage investment in Oracle IT • Eliminate data duplication - Eliminate separate analytical servers Deliver enterprise-wide “predictive” applications Don’t move the Data Traditional ML Hours, Days or Weeks Data Extraction Data Prep & Transformation Data Mining Model Building Data Mining Model “Scoring” Data Prep. & Transformation Data Import avings Model “Scoring” Embedded Data Prep Data Preparation Model Building Oracle’s in-DB Machine Learning Secs, Mins or Hours ORACLE AUTONOMOUS DATABASE
  • 48. Increasing sources of relevant data can boost model accuracy More Data Variety—Better Predictive Models Model with 20 variables Model with “Big Data” and hundreds -- thousands of input variables including: • Demographic data • Purchase POS transactional data • “Unstructured data”, text & comments • Spatial location data • Long term vs. recent historical behavior • Web visits • Sensor data • etc. Naïve Guess or Random 100% 0% Population Size Responders Model with 75 variables Model with 250 variables 100% Engineered Features – Derived attributes/variable that reflect domain knowledge—key to best models
  • 49. First, Identify the Key Attributes That Most Influence the Target Attribute Modeling and Machine Learning Attribute Importance Model
  • 50. Next, Build Predictive Models to Predict Customers who are Likely to Have Good_Credit Modeling and Machine Learning Split Data into Train and Test Build and Test Classification Model
  • 51. Test the ML model’s accuracy • Randomly selected “hold out” sample of data that was used to train the ML model • Compute Cumulative Gains, Lift, Accuracy, etc. • Review the attributes used in the model and model coefficients • Make sure the model makes sense Next, Build Predictive Models to Predict Customers who are Likely to Have Good_Credit Model Evaluation (Machine Learning) Model Evaluation
  • 52. Simple SQL Apply scripts run 100% inside the Database for immediate ML model deployment Apply the Models to Predict “Best Customers” Deployment Model Apply/”Scoring”
  • 55. Manage and Analyze All Your Data Big Data SQL / R SQL / R / Python Object Store “Engineered Features” – Derived attributes that reflect domain knowledge—key to best models e.g.: • Counts • Totals • Changes over time Boil down the Data Lake Architecturally, lots of options and flexibility
  • 56. In-Database Machine Learning More Models Better Models Faster, More Secure Less Cost Ready to Deploy! No Need To Extract and Move Data Data stays in Database Zero time required. No production impact. Data Preparation and Transformation Accelerated with Automatic Data Prep No separate environment required. Much faster data prep. Data stays protected and secured. Data Mining and Model Building SQL, R, Python Oracle Data Miner UI OML Notebooks Oracle Data Miner and AutoML greatly speed model building. Less skill required. No coding. No Need to Transform Production Data Embedded Data Preparation No need for second production instance. Model Scoring Accelerated Via Exadata Database Machine Faster model validation Easy to repeat model building as often as needed
  • 57. • OAA (Oracle Data Mining + Oracle R Enterprise) and ORAAH combined • OAA includes support for Partitioned Models, Transactional, Unstructured, Geo-spatial, Graph data. etc, Oracle’s Machine Learning & Adv. Analytics Algorithms CLASSIFICATION • Naïve Bayes • Logistic Regression (GLM) • Decision Tree • Random Forest • Neural Network • Support Vector Machine • Explicit Semantic Analysis CLUSTERING • Hierarchical K-Means • Hierarchical O-Cluster • Expectation Maximization (EM) ANOMALY DETECTION • One-Class SVM TIME SERIES • State of the art forecasting using Exponential Smoothing • Includes all popular models e.g. Holt-Winters with trends, seasons, irregularity, missing data REGRESSION • Linear Model • Generalized Linear Model • Support Vector Machine (SVM) • Stepwise Linear regression • Neural Network • LASSO * 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 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 PACKAGES • CRAN R Algorithm Packages through Embedded R Execution • Spark MLlib algorithm integration EXPORTABLE ML MODELS • REST APIs for deployment X 1 X 2 A 1 A 2 A 3 A 4 A 5 A 6 A 7
  • 58. ANALYTICAL SQL • SQL Windows • SQL Aggregate functions • LAG/LEAD functions • SQL for Pattern Matching • Additional approximate query processing: APPROX_COUNT , APPROX_SUM, APPROX_RANK • Regular Expressions • Linear regression • ANOVA (Analysis of variance) • Test Distribution fit (e.g. Normal distribution test, Binomial test, Weibull test, Uniform test, Exponential test, Poisson test, etc.) • Statistical Aggregates (min, max, mean, median, stdev, mode, quantiles, plus x sigma, minus x sigma, top n outliers, bottom n outliers) STATISTICAL FUNCTIONS • Descriptive statistics (e.g. median, stdev, mode, sum, etc.) • Hypothesis testing (t-test, F-test, Kolmogorov- Smirnov test, Mann Whitney test, Wilcoxon Signed Ranks test • Correlations analysis (parametric and nonparametric e.g. Pearson’s test for correlation, Spearman's rho coefficient, Kendall's tau-b correlation coefficient) • Ranking functions • Cross Tabulations with Chi-square statistics| Oracle’s Machine Learning & Adv. Analytics Algorithms
  • 59. Algorithms Operate on Data ML and AI are just “Algorithms” Move the Algorithms; Not the Data!; It Changes Everything!
  • 60. Thank You Any Questions ? Sandesh Rao VP AIOps for the Autonomous Database @sandeshr https://www.linkedin.com/in/raosandesh/ https://www.slideshare.net/SandeshRao4