Oracle Machine Learning Overview and From Oracle Data Professional to Oracle Data Scientist in 6 Weeks!

Oracle Machine Learning
From Oracle Data Professional to Oracle Data Scientist
Charlie Berger
Sr. Director Product Management, Machine Learning, AI and CognitiveAnalytics,
charlie.berger@oracle.com www.twitter.com/CharlieDataMine
Copyright © 2019 Oracle and/or its affiliates.
Move the Algorithms; Not the Data!

Safe harbor statement
The following is intended to outline our general product direction. It is intended for information purposes
only, and may not be incorporated into any contract. It is not a commitment to deliver any material,
code, or functionality, and should not be relied upon in making purchasing decisions.
The development, release, timing, and pricing of any features or functionality described for Oracle’s
products may change and remains at the sole discretion of Oracle Corporation.
2

Goal
Share an attainable, logical, evolutionary path for Oracle data
professionals to add machine learning to their valuable Oracle
data skills to extract more information, insights and to make
predictions.
Copyright © 2020 Oracle and/or its affiliates.Copyright © 2020 Oracle and/or its affiliates.

Oracle Database Converged Features

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

Operational DBAs spend a lot of time…
85% of security breaches
occurred after the CVE was
published
DB Maestro
Security
85%
91% experience unplanned
data center outages
Healthcare IT News
Database downtime costs
$7,900 / minute
DB Maestro
Reliability
91%
72% of IT budget is spent on
generic maintenance tasks vs
innovation
ComputerWorld
Maintenance
72%

Oracle Autonomous Database Can Help
7
Self-Driving
Automates all database and
infrastructure management,
monitoring, tuning
Self-Securing
Protects from both
external attacks and
malicious internal users
Self-Repairing
Protects from all
downtime including
planned maintenance
Oracle Autonomous Database does the grunt work forYOU!

HIGHER AGILITY
LOWER RISK You are more
VALUABLE
Automation moves DBA up the value chain…

Data Engineer
Architecture,
“data wrangler”
Machine Learning
Solving data-driven
problems
Discovering insights
Making predictions
Data Security
Data classification,
Data life-cycle
mgmt
ApplicationTuning
SQL tuning,
connection mgmt
The Evolution of the DBA/Database Developer Role

“Why Oracle?
Because that’s where the
data is!”
Larry Ellison, Executive
Chairman and CTO of Oracle
Corporation

Algorithms automatically sift through large amounts of data to discover hidden
patterns, new insights and make predictions
What is Machine Learning?
Identify most important factor (Attribute Importance)
Predict customer behavior (Classification)
Find profiles of targeted people or items (Classification
Predict or estimate a value (Regression)
Segment a population (Clustering)
Find fraudulent or “rare events” (Anomaly Detection)
Determine co-occurring items in a “basket” (Associations)
X1
X2
A1A2A3A4 A5A6 A7
SupervisedLearningUnsupervisedLearning

Example Machine Learning Use Cases
http://www.slideshare.net/bigdataelephants/big-data-elephants-strategic-consulting-engineering-services-34175779

Machine Learning Algorithms Need Data
Move the Algorithms, Not the Data!
3
An “AI Database”? A “Thinking Database”? It Changes Everything!

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

Operationalizing and Embedding ML
Length of time to put a model into
production. Based on 141
respondents who stated they are
doing this today
How long does it take to put a defined model into operational use?
?
?
?

Why Do 87% of Data Science Projects Never
Make It Into Production?
But now that it’s a team sport, … work is now being
embedded into the fabric of the company, it’s
essential that every person on the team is able to
collaborate with everyone else: the data engineers,
the data stewards, people that understand the data
science, or analytics, or BI specialists, all the way up
to DevOps and engineering.
“This is a big place that holds companies back because they’re not used to
collaborating in this way,” Leff says. “Because when they take those insights, and they
flip them over the wall, now you’re asking an engineer to rewrite a data science
model created by a data scientist, how’s that work out, usually?”
“Well,” Chapo says, “It doesn’t.”
“Oftentimes people imagine a world where we’re doing this amazing, fancy, unicorn,
sprinkling-pixie-dust sort of AI projects,” he said.
https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/

OML Functionality and
Supported Languages

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

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
CrossTabulations with Chi-square
statistics
Linear regression
ANOVA (Analysis of variance)
Test Distribution fit
(e.g., Normal distribution test,
Binomial test, Weibull test,
Uniform test, Exponential test,
Poisson test)
Statistical Aggregates
(min, max, mean, median, stdev,
mode, quantiles, plus x sigma,
minus x sigma, top n outliers,
bottom n outliers)
Statistical Functions and Analytical SQL
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

Goal: Manage and Analyze All Your Data
Big Data SQL
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,
Many Options
and Flexibility
Coming soon

OML for SQL Model Build & SQL Apply
BEGIN
DBMS_DATA_MINING.CREATE_MODEL(
model_name => 'BUY_INSUR1',
mining_function => dbms_data_mining.classification,
data_table_name => 'CUST_INSUR_LTV',
case_id_column_name => 'CUST_ID',
target_column_name => 'BUY_INSURANCE',
settings_table_name => 'CUST_INSUR_LTV_SET');
END;
Simple SQL Syntax—Classification Model
Select prediction_probability(BUY_INSUR1, 'Yes'
USING 3500 as bank_funds, 825 as checking_amount, 400 as credit_balance, 22 as age,
'Married' as marital_status, 93 as MONEY_MONTLY_OVERDRAWN, 1 as house_ownership)
from dual;
ML Model Build (PL/SQL)
Model Apply (SQL query)

OML for SQL Model Build
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_Model_Settings');
END;
Simple SQL Syntax—Attribute Importance
SELECT attribute_name, explanatory_value, rank
FROM BUY_INSURANCE_AI
ORDER BY rank, attribute_name;
ML Model Build (PL/SQL)
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 A5 A6 A7

OML for R Model Build
> ore.odmAI (BUY_INSURANCE ~ ., CUST_INSUR_LTV)
Call:
ore.odmAI(formula = BUY_INSURANCE ~ ., data = CUST_INSUR_LTV)
Simple R Language Syntax—Attribute Importance
ML Model Build (R)
Model Results (R)
Importance:
importance rank
BANK_FUNDS 0.2161187797 1
MONEY_MONTLY_OVERDRAWN 0.1489347141 2
N_TRANS_ATM 0.1463026512 3
N_TRANS_TELLER 0.1155879786 4
T_AMOUNT_AUTOM_PAYMENTS 0.1095178647 5
A1A2A3A4 A5A6 A7

OML for Python Model Build—Coming soon!
> ai_mod = ai(**setting) # Create AI model object
> ai_mod = ai_mod.fit(train_x, train_y)
Simple Python Language Syntax—Attribute Importance
ML Model Build (Python)
Model Results (Python)
Importance:
variable importance rank
BANK_FUNDS 0.2161187797 1
MONEY_MONTLY_OVERDRAWN 0.1489347141 2
N_TRANS_ATM 0.1463026512 3
N_TRANS_TELLER 0.1155879786 4
T_AMOUNT_AUTOM_PAYMENTS 0.1095178647 5
A1A2A3A4 A5A6 A7

Oracle Data Miner UI
Easy to use to define
analytical
methodologies that
can be shared
SQL Developer
Extension
Workflow API
and generates SQL
code for immediate
deployment
Drag and Drop,Workflows, Easy to Use UI for “Citizen Data Scientist”

OML4R
R languageSQL
“push down”
Transparency layer for
“push down” to
equivalent SQL for
parallelized in-DB
processing
Direct access to
DB data
ROracle pkg for OCI
connectivity
“Embedded R” call
outs to R packages
R Language API to OML Algorithms and Integration with R

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

Oracle Machine Learning for R / Python
Transparency layer
‐ Leverage proxy objects so data remain in database
‐ Overload native functions translating functionality to SQL
‐ Use familiar R/Python syntax to manipulate 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, Automated Machine Learning - AutoML
‐ Feature selection, model selection, hyper-parameter tuning
Multiple Components/APIs of Oracle Machine Learning
Database
Server
Client
SQL Interfaces
SQL*Plus
SQLDeveloper
OML4Py OML4R
* Coming soon

Coming Soon! | AutoML – new with OML4Py
Auto Feature Selection
– Reduce # of features by
identifying most predictive
– Improve performance
and accuracy
Increase data scientist productivity – reduce overall compute time
Auto Algorithm
Selection
Much faster than
exhaustive search
Auto Feature
Selection
De-noise data and
reduce # of features
AutoTune
Significant accuracy
improvement
Auto Algorithm Selection
– Identify in-database
algorithm that achieves highest
model quality
– Find best algorithm faster
than with exhaustive search
AutoTune Hyperparameters
– Significantly improve
model accuracy
– Avoid manual or exhaustive
search techniques
Enables non-expert users to leverage Machine Learning
Data
Table ML Model

Coming Soon! | OML AutoML User Interface
Automate production and deployment of ML models
Enhance Data Scientist productivity
and user-experience
Enable non-expert users to leverage ML
Unify model deployment and monitoring
Support model management
Features
Minimal user input: data, target
Model leaderboard
Model deployment via REST
Model monitoring
Cognitive features for image and text
“Code-free” user interface supporting automated end-to-end machine learning

Coming Soon! | Algorithms for Database 20c
Gradient BoostedTrees (XGBoost)
Highly popular and powerful algorithm – Kaggle winners
Classification, regression, ranking, survival analysis
MSET-SPRT
Multivariate State EstimationTechnique - Sequential
Probability RatioTest (MSET-SPRT)
Nonlinear, nonparametric anomaly detection
algorithm designed to monitor critical processes.
Detects subtle anomalies while also producing
minimal false alarms.
Calibrates expected behavior from historical normal
operational sequence of monitored signals.
Re-implemented and sped up in-DB and based on original
Oracle Labs algorithm
Two major new ML algorithms

OracleApplications
that Embed Oracle Machine
Learning Algorithms

Enabling Predictive Enterprise Applications
Integrated data management + embedded
predictive analytics
Full 360 degree employee view
Single source of HCM data data
Interactive dashboards and “What if” analysis
Customizable if desired to add input variables
to predictive models
Mobile + Oracle Cloud solutions
HCM PredictiveWorkforce
37
Additional relevant data and
“engineered features”
Sensor data, Text, unstructured data,
transactional data, spatial data, etc.
Historical data Assembled
historical data
Historical or Current Data to be “scored” for
predictions
Predictions & Insights
Oracle Database
Link to HCM PredictiveWorkforce demoCopyright © 2020 Oracle and/or its affiliates.

Oracle Adaptive Intelligent (AI) Apps for Manufacturing
Insights (Patterns and CorrelationsAnalysis)
– Discover key influencers and patterns that
affect yield & quality
Predictive Analytics
– Predictive critical outcomes during
manufacturing to minimize losses
Reasons why using/like OAA’s ML
– Easy-to-integrate R & PL/SQLAPIs for many
ML algorithms
– In-database execution & scalable
performance
– Enterprise grade support for OAA ML
– GA Q4FY18
Achieve Manufacturing Operational Excellence using Machine Learning & AI

From Database Developer to
Data Scientist in 6Weeks!
The Changing Role of the
DBA
https://www.datacamp.com/community/blog/data-scientist-vs-data-engineer
https://www.kdnuggets.com/2020/02/poll-automl-replace-data-scientists.html

Database Developer to Data Scientist Journey
Data extraction
Data wrangling Typically 80% of the work!
Deriving new attributes
(“feature engineering”)
…
…
Import predictions & insights
Translate and deploy ML models Eliminated or minimized w/ Oracle
Automate
You are Likely Already Doing Much ofTheWork!
1 - https://www.infoworld.com/article/3228245/data-science/the-80-20-data-science-dilemma.html
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
Data Management platform becomes combined/hybrid data
management + machine learning platform
Where the Machine Learning “Magic” Happens

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

Six Major Steps
DATA UNDERSTANDING
Assemble the “right data”
Data profiling
• Data visualization
• Univariate statistics/group by
• Bi-variate statistics
DATA PREPARATION
Sampling/Stratified
Algorithm req’d transforms
• Auto Data Preparation
• MissingValues, Binning, Normalization,
etc.
• Unstructured data
• Aggregations
Domain specific transforms
• “Engineered Features”
Features Selection
MODELING
Algorithm settings/defaults
• Stratified sampling
• Feature selection
• Build model(s)
EVALUATION
Model evaluation
Model comparison
Model selection
DEPLOYMENT
In-DB ML model apply
• Real-time ML apply
• In-database, REST
Embed methodology
• Applications
• Dashboards
Well-defined
business problem

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

Database Developer to Data Scientist Journey
• Business Understanding—Week 1
• Data Understanding—Week 2
• Data Preparation—Week 3
• Modeling (ML)—Week 4
• Evaluation—Week 5
• Deployment—Week 6
Six Major Steps (Oracle Machine Learning POV)
https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining

SQL Developer Extension: Oracle Data Miner UI
Business Understanding: Target customers most likely to Buy Insurance
Data Understanding
Modeling (ML)
Evaluation
Deployment
Data Preparation

Week 1—Business Understanding
• Predict employees that voluntarily churn
• Predict customers that are likely to churn
• Target “best” customers
• Find items that will help me sell more most profitable items
• What is a specific customer most likely to purchase next?
• Who are my “best customers”?
• How can I combat fraud?
• I’ve got all this data; can you “mine” it and find useful insights?
47
Start with aWell-Defined Business Problem Statement

“If I had an hour to solve a problem I'd spend
55 minutes thinking about the problem and 5
minutes thinking about solutions.”
― Albert Einstein
Start with aWell-Defined Business Problem Statement

The Sand Trap of Poorly Formed
Problem Statements
I’ve got all this data; can you
“mine” it and find useful insights?

Be Extremely Specific in Problem Statement
Poorly Defined Better ML Function
Predict employees that leave •Based on past employees that voluntarily left:
• Create New Attribute EmplTurnover  O/1 Classification
Predict customers that churn •Based on past customers that have churned:
• Create New Attribute Churn  YES/NO Classification
Target “best” customers •Recency, Frequency Monetary (RFM) Analysis
•Specific Dollar Amount over Time Window:
• Who has spent $500+ in most recent 18 months Classification
How can I make more $$? •What helps me sell soft drinks & coffee? Association
Rules
Which customers are likely to buy? •How much is each customer likely
to spend? Regression
Who are my “best customers”? •What descriptive “rules” describe “best
customers”? Classification
How can I combat fraud? •Which transactions are the most anomalous?
• Then roll-up to physician, claimant, employee, etc.
Anomaly
Detection X1
X2

Target “best” customers who have GOOD CREDIT and make payments
51
Be Extremely Specific in your Problem Statement

“Good_Credit” customers who complete all
their payments are hard to find.

Week 2—Data Understanding
53
Review the Data; Does it Makes Sense?
AreAGEs all positive, 0-120?
Are INCOME values weekly or monthly?
Are the LOAN_AMOUNTS reasonable?
Etc….

54
Simple, exploratory graphs to understand the
data

55
AreAGEs all positive, 0-120?
Are INCOME values weekly or monthly?
Are the LOAN_AMOUNTS reasonable?
Etc….

Week 3—Data Preparation
Prepare the Data, Create New Derived Attributes or “Engineered Features”
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)

Oracle Data Miner’s Column Filter Node does automated data profiling to
highlight issues and make recommendations
– Missing values
– Outliers
– Too many distinct values
– Too many constants
– Correlated data
57

Oracle Machine Learning’s Auto Data Prep (ADP) and ML algorithms are
designed with intelligent defaults and can automatically deal with:
– Missing values
– Outliers
– Binning
– Too many distinct values
– Too many constants
– Trans data/aggregations
– Unstructured data
– Correlated data
58

Week 4—Modeling (Machine Learning)
First, Identify the KeyAttributesThat Most Influence theTarget Attribute

Training andTesting ML Models using 60/40% Random Samples
Historical DataTrain Test
Build Model Test Model Evaluate ModelTrain ModelHistorical Data

Build multiple models with different algorithms and settings

Week 5—Model Evaluation (ML)
Randomly selected “hold
out” sample of data that
was used to train the ML
model
ComputeCumulative Gains,
Lift, Accuracy, etc.
Review the attributes used
in the model and model
coefficients
Make sure the model makes
sense
62
Next, test model accuracy
Model Evaluation

Week 6—Deployment
Simple SQL Apply
scripts run 100% inside
the Database for
immediate ML model
deployment
Apply the Models to Predict “Best Customers”
Model Apply/”Scoring”

Week 6—Deployment
Simple SQL Apply scripts run 100%
inside the Database for model build,
model apply and immediate ML model
deployment
Apply the Models to Predict “Best Customers”
Model Build
Model Apply
Results

Congratulations!
You are an Oracle Data
Scientist!
Data Scientist

Wait, there is more!

OML + APEX
Interactively Explore Data and OML Insights and Predictons
Predictions

OML + Analytics Cloud
Predictions, Probabilities and Insights
* Oracle AnalyticsCloud screen from “Predicting a GoodWine” by Francesco
Tisiot, Rittman Mead and Charlie Berger, OracleCopyright © 2020 Oracle and/or its affiliates.

OML + Analytics Cloud
* Oracle AnalyticsCloud screen from “Predicting a GoodWine” by Francesco
Tisiot, Rittman Mead and Charlie Berger, OracleCopyright © 2020 Oracle and/or its affiliates.
Predictions, Probabilities and Insights

ML Model Deployment via ORDS REST API
Launch Development APEX

For More Information
Google: Oracle Machine Learning on OTN
https://www.oracle.com/machinelearning
71Copyright © 2019 Oracle and/or its affiliates.Copyright © 2019 Oracle and/or its affiliates.

Where should I start?
Quick Starts, HOLs, Docs and Oracle
Learning Library Tutorials
• Hands-0n Lab: How to Pick a Good Wine for $30<
using Oracle Autonomous Database, Oracle Machine
Learning, APEX, Oracle Analytics Cloud and REST
Services
• Oracle Machine Learning for R Learning Path
• Autonomous Data Warehouse For Developers. Get
Hands on with Oracle Public Cloud
• Learn How to Use Oracle Data Miner UI in 45 Minutes
• Hands-on Lab: Learn to Use Oracle Machine Learning
Notebooks
• OML Getting Started Documentation:

ThankYou
Charlie Berger
Senior Director, Product Management
Machine Learning, AI and Cognitive Analytics

Oracle Machine Learning Overview and From Oracle Data Professional to Oracle Data Scientist in 6 Weeks!

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