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Enterprise AI by using IBM DB2
ENTERPRISE AI
OBJECT AUTOMATION SYSTEM SOLUTIONS PVT. LTD
3
1
2
3
AUTOMATOR DECIDER RECOMMENDER ILLUMINATOR EVALUATOR
EXAMPLES EXAMPLES EXAMPLES EXAMPLES EXAMPLES
When AI has all
the context and
needs to quickly
reach a
conclusion..
AI should decide
and implement .
When AI has
plenty of context,
but an human
touch is needed for
execution… AI
should decide, and
humans should
implement.
When there are
multiple repetitive
decisions to be
made, but AI is
missing necessary
context.. AI should
recommend, and
humans should
decide.
When inherently
creative work will
benefit from
machine learning…
humans should
leverage
AI-generated
insights.
When there’s not
enough context,
and the stakes are
high…
humans should
generate scenarios
for AI to evaluate.
Dynamic Pricing
Engines,
Algorithmic Add
displays
Predictive
Maintenance, Call
center optimization
Promotional
Calendar creation,
Sales and
operation Planning
Product design
based on customer
usage
Large seasonal
promotions, Digital
twin simulation for
operation
AI at Scale
What are the benefits of AI in the enterprise?
Better
Quality
Better
Talent
Management
Business
model
innovation
and
expansion.
Improved
customer
services
Improved
Monitoring
Faster
Product
Development.
Enterprise
AI
Applications of AI at Work
01 05
02
03
06
07
Customer Experience Service
and Support
Targeted Marketing
Smarter supply chains
Quality Control and Quality
Assurance
Contextual Understanding
Optimization
04 08
Safe and Smart operations More Effective Learning
Enterprise AI by using IBM DB2
ENTERPRISE AI
Enterprise AI by using IBM DB2
ENTERPRISE AI
Regulated Data
Data Volume
Noisy Data
ML
Development
Challenges
ENTERPRISE AI
Hosting
Speed
Integration
ML
Deployment
Challenges
51% AI projects don’t go beyond experiments
ENTERPRISE AI
IBM – DB2 supports
In-Database Machine
Learning
Latency sensitive
Decisions
Large Batch Predictions
Instantaneous predictions
Examples:
• Payment processing
• Fraud detection
• Loan/claim pre-approval
Real-time prediction using “fresh” and
large operational data
Examples:
• Anomaly detection
• Escalation risk prediction
• Dynamic price optimization
ENTERPRISE AI
ENTERPRISE AI
Accelerating and Optimizing AI lifecycle with IBM DB2
01 02
Integrating Open
Source models with
DB2
Developing and
Deploying DB2-Native
ML models
BRING YOUR OPEN-SOURCE MODELS TO DB2
SOLUTION 1:
ENTERPRISE AI
PYTHON UDF : PYTHON MODELS VIA DB2
Export the ML pipeline
by serializing python
joblib
Db2 Server
Host OS
Db2
Instance
Python
Runtim
e
ENTERPRISE AI
ENTERPRISE AI
In-db Inferencing
Benefits
– ML Infrastructure
– Low-latency
– High-throughput
– Simpler Integration
ENTERPRISE AI
SOLUTION 1 - DEMO
AI & MACHINE LEARNING IN THE DATABASE
WORLD.


 Sivakumar Shanmugam

DatabaseArchitect
 11/12/2022
ABOUT WERNER
COMPANY HISTORY
DATABASE SETUP
Install & setup Db2 V 11.5 fp 5 on Linux
Environment
STEP 2. DATA EXPLORATION
Use these stored procedures to evaluate the content of the given
data
 IDAX.SUMMARY1000 - Summary of up to 1000 columns
 IDAX.COLUMN_PROPERTIES - Create a column properties table
 IDAX.GET_COLUMN_LIST - Get a list of columns
STEP 3 : DATA TRANSFORMATION
 Use the following stored procedures to transform the data before
passing it to a machine learning algorithm.
 IDAX.IMPUTE_DATA - Impute missing data
 IDAX.SPLIT_DATA - Split data into training data and test data
 IDAX.STD_NORM - Standardize or normalize columns of the input table
 IDAX.EFDISC - Discretization bins of equal frequency
 IDAX.APPLY_DISC - Discretize data by using limits for discretization bins
STEP 4 : MODEL BUILDING
 Use these stored procedures to build machine learning models.
 Decision trees - IDAX.GROW_DECTREE A decision tree is a hierarchical, graphical structure
accurately classify a model.
 Linear regression - IDAX.LINEAR_REGRESSION is the most commonly used method of predictive
analysis.
 Naive Bayes IDAX.NAIVEBAYES - The Naive Bayes classification algorithm is a probabilistic
classifier.
 K-means clustering IDAX.KMEANS - The K-means algorithm is the most widely used clustering
algorithm
STEP 5 : MODEL EVALUATION
 Use these stored procedures to evaluate the performance of your model by comparing predictions to the
true values.
 IDAX.CMATRIX_STATS - Calculate classification quality factors from a confusion matrix
 IDAX.CONFUSION_MATRIX - Build a confusion matrix
 IDAX.MAE - Calculate the mean absolute error of regression predictions
 IDAX.MSE - Calculate the mean squared error of regression predictions
STEP 6 : MODEL INFERENCING/ DEPLOYMENT
 Use these stored procedures to make predictions with your trained machine learning model.
 IDAX.PREDICT_DECTREE - Apply a decision tree model
 IDAX.PREDICT_KMEANS - Apply a K-means clustering model to new data
 IDAX.PREDICT_LINEAR_REGRESSION - Apply a linear regression model to a target
 IDAX.PREDICT_NAIVEBAYES - Apply a Naive Bayes model to new data
IBM DB2 V 11.5.6
database
COLLECT DATA
Data feed from Trucks to Databases
IBM DB2 V
11.5.6
database
LINK
DEMO 1 - PREDICTIVE MAINTENANCE OPTIMIZATION OF TRUCK FLEET
DATA SET
TRUCK FLEET DATA SET - Data resource – https://github.com
/Predictive_Maintenance_Optimization/branches
MACHINE LEARNING PROCESS ON DB2
OPPORTUNITIES FOR PREDICTIVE MODELING
 How long the truck can run without maintenance ?
 How many drivers can quit in the next 30 days ?
 Company turnover in future ?
 Accident Prevention ?
 Battery life on the trucks ?
 Weather alerts to drivers - k-means algorithm?
 Run machine learning set up on AIX-Open shift environment (Testing in progress)
Q & A
Q & A
srikamani@gmail.com
jssivakumar@hotmail.com
OBJECT AUTOMATION SYSTEM SOLUTIONS PVT. LTD.

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Enterprise AI by using IBM DB2

  • 2. ENTERPRISE AI OBJECT AUTOMATION SYSTEM SOLUTIONS PVT. LTD
  • 3. 3 1 2 3 AUTOMATOR DECIDER RECOMMENDER ILLUMINATOR EVALUATOR EXAMPLES EXAMPLES EXAMPLES EXAMPLES EXAMPLES When AI has all the context and needs to quickly reach a conclusion.. AI should decide and implement . When AI has plenty of context, but an human touch is needed for execution… AI should decide, and humans should implement. When there are multiple repetitive decisions to be made, but AI is missing necessary context.. AI should recommend, and humans should decide. When inherently creative work will benefit from machine learning… humans should leverage AI-generated insights. When there’s not enough context, and the stakes are high… humans should generate scenarios for AI to evaluate. Dynamic Pricing Engines, Algorithmic Add displays Predictive Maintenance, Call center optimization Promotional Calendar creation, Sales and operation Planning Product design based on customer usage Large seasonal promotions, Digital twin simulation for operation AI at Scale
  • 4. What are the benefits of AI in the enterprise? Better Quality Better Talent Management Business model innovation and expansion. Improved customer services Improved Monitoring Faster Product Development. Enterprise AI
  • 5. Applications of AI at Work 01 05 02 03 06 07 Customer Experience Service and Support Targeted Marketing Smarter supply chains Quality Control and Quality Assurance Contextual Understanding Optimization 04 08 Safe and Smart operations More Effective Learning
  • 9. ENTERPRISE AI Regulated Data Data Volume Noisy Data ML Development Challenges
  • 11. ENTERPRISE AI IBM – DB2 supports In-Database Machine Learning
  • 12. Latency sensitive Decisions Large Batch Predictions Instantaneous predictions Examples: • Payment processing • Fraud detection • Loan/claim pre-approval Real-time prediction using “fresh” and large operational data Examples: • Anomaly detection • Escalation risk prediction • Dynamic price optimization ENTERPRISE AI
  • 13. ENTERPRISE AI Accelerating and Optimizing AI lifecycle with IBM DB2 01 02 Integrating Open Source models with DB2 Developing and Deploying DB2-Native ML models
  • 14. BRING YOUR OPEN-SOURCE MODELS TO DB2 SOLUTION 1:
  • 15. ENTERPRISE AI PYTHON UDF : PYTHON MODELS VIA DB2 Export the ML pipeline by serializing python joblib Db2 Server Host OS Db2 Instance Python Runtim e
  • 17. ENTERPRISE AI In-db Inferencing Benefits – ML Infrastructure – Low-latency – High-throughput – Simpler Integration
  • 19. SOLUTION 1 - DEMO
  • 20. AI & MACHINE LEARNING IN THE DATABASE WORLD.    Sivakumar Shanmugam  DatabaseArchitect  11/12/2022
  • 23. DATABASE SETUP Install & setup Db2 V 11.5 fp 5 on Linux Environment
  • 24. STEP 2. DATA EXPLORATION Use these stored procedures to evaluate the content of the given data  IDAX.SUMMARY1000 - Summary of up to 1000 columns  IDAX.COLUMN_PROPERTIES - Create a column properties table  IDAX.GET_COLUMN_LIST - Get a list of columns
  • 25. STEP 3 : DATA TRANSFORMATION  Use the following stored procedures to transform the data before passing it to a machine learning algorithm.  IDAX.IMPUTE_DATA - Impute missing data  IDAX.SPLIT_DATA - Split data into training data and test data  IDAX.STD_NORM - Standardize or normalize columns of the input table  IDAX.EFDISC - Discretization bins of equal frequency  IDAX.APPLY_DISC - Discretize data by using limits for discretization bins
  • 26. STEP 4 : MODEL BUILDING  Use these stored procedures to build machine learning models.  Decision trees - IDAX.GROW_DECTREE A decision tree is a hierarchical, graphical structure accurately classify a model.  Linear regression - IDAX.LINEAR_REGRESSION is the most commonly used method of predictive analysis.  Naive Bayes IDAX.NAIVEBAYES - The Naive Bayes classification algorithm is a probabilistic classifier.  K-means clustering IDAX.KMEANS - The K-means algorithm is the most widely used clustering algorithm
  • 27. STEP 5 : MODEL EVALUATION  Use these stored procedures to evaluate the performance of your model by comparing predictions to the true values.  IDAX.CMATRIX_STATS - Calculate classification quality factors from a confusion matrix  IDAX.CONFUSION_MATRIX - Build a confusion matrix  IDAX.MAE - Calculate the mean absolute error of regression predictions  IDAX.MSE - Calculate the mean squared error of regression predictions
  • 28. STEP 6 : MODEL INFERENCING/ DEPLOYMENT  Use these stored procedures to make predictions with your trained machine learning model.  IDAX.PREDICT_DECTREE - Apply a decision tree model  IDAX.PREDICT_KMEANS - Apply a K-means clustering model to new data  IDAX.PREDICT_LINEAR_REGRESSION - Apply a linear regression model to a target  IDAX.PREDICT_NAIVEBAYES - Apply a Naive Bayes model to new data
  • 29. IBM DB2 V 11.5.6 database
  • 30. COLLECT DATA Data feed from Trucks to Databases IBM DB2 V 11.5.6 database
  • 31. LINK DEMO 1 - PREDICTIVE MAINTENANCE OPTIMIZATION OF TRUCK FLEET DATA SET
  • 32. TRUCK FLEET DATA SET - Data resource – https://github.com /Predictive_Maintenance_Optimization/branches
  • 34. OPPORTUNITIES FOR PREDICTIVE MODELING  How long the truck can run without maintenance ?  How many drivers can quit in the next 30 days ?  Company turnover in future ?  Accident Prevention ?  Battery life on the trucks ?  Weather alerts to drivers - k-means algorithm?  Run machine learning set up on AIX-Open shift environment (Testing in progress)
  • 35. Q & A Q & A srikamani@gmail.com jssivakumar@hotmail.com
  • 36. OBJECT AUTOMATION SYSTEM SOLUTIONS PVT. LTD.

Editor's Notes

  1. https://www.ibm.com/support/producthub/db2/docs/content/SSEPGG_11.5.0/com.ibm.db2.luw.ml.doc/doc/c_model_build.html