This document discusses enterprise artificial intelligence and machine learning solutions provided by Object Automation System Solutions Pvt. Ltd. It describes different types of AI models like automator, decider, recommender, and illuminator that are suited for various business use cases. It also lists some benefits of AI in enterprises like better quality, talent management, business model innovation, improved customer services, and faster product development. Finally, it demonstrates how machine learning can be implemented using IBM DB2 database by collecting truck fleet data, building predictive models for opportunities like predictive maintenance, and deploying models for predictions.

1. ENTERPRISE AI
OBJECT AUTOMATION SYSTEM SOLUTIONS PVT. LTD

2. 2
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

3. 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

4. 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

6. ENTERPRISE AI

8. ENTERPRISE AI
Regulated Data
Data Volume
Noisy Data
ML
Development
Challenges

9. ENTERPRISE AI
Hosting
Speed
Integration
ML
Deployment
Challenges
51% AI projects don’t go beyond experiments

10. ENTERPRISE AI
IBM – DB2 supports
In-Database Machine
Learning

11. 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

12. 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

13. BRING YOUR OPEN-SOURCE MODELS TO DB2
SOLUTION 1:

14. 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

15. ENTERPRISE AI

16. ENTERPRISE AI
In-db Inferencing
Benefits
– ML Infrastructure
– Low-latency
– High-throughput
– Simpler Integration

17. ENTERPRISE AI

18. SOLUTION 1 - DEMO

19. AI & MACHINE LEARNING IN THE DATABASE
WORLD.


 Sivakumar Shanmugam

DatabaseArchitect
 11/12/2022

20. ABOUT WERNER

21. COMPANY HISTORY

22. DATABASE SETUP
Install & setup Db2 V 11.5 fp 5 on Linux
Environment

23. 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

24. 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

25. 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

26. 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

27. 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

28. IBM DB2 V 11.5.6
database

29. COLLECT DATA
Data feed from Trucks to Databases IBM DB2 V
11.5.6 database

30. LINK
DEMO 1 - PREDICTIVE MAINTENANCE OPTIMIZATION OF TRUCK FLEET
DATA SET

31. TRUCK FLEET DATA SET - Data resource – https://github.com
/Predictive_Maintenance_Optimization/branches

32. MACHINE LEARNING PROCESS ON DB2

33. 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)

34. Q & A
Q & A
srikamani@gmail.com
jssivakumar@hotmail.com

35. OBJECT AUTOMATION SYSTEM SOLUTIONS PVT. LTD.