1.
ENTERPRISE AI
OBJECT AUTOMATION SYSTEM SOLUTIONS PVT. LTD

2.
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

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

5.
ENTERPRISE AI

6.
ENTERPRISE AI
Regulated Data
Data Volume
Noisy Data
ML
Development
Challenges

7.
ENTERPRISE AI
Hosting
Speed
Integration
ML
Deployment
Challenges
51% AI projects don’t go beyond experiments

8.
ENTERPRISE AI
IBM – DB2 supports
In-Database Machine
Learning

9.
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

10.
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

11.
BRING YOUR OPEN-SOURCE MODELS TO DB2
SOLUTION 1:

12.
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

13.
SOLUTION 1 - DEMO

14.
ENTERPRISE AI

15.
Connecting to IBM Power9 system
(Vina in university of Oregon)
ENTERPRISE AI

16.
 Testing the Db2 setup

17.
Importing necessary Libraries in
Jupyter notebook
ENTERPRISE AI

18.
Connecting to db2
ENTERPRISE AI

19.
Accuracy Report
ENTERPRISE AI

20.
BUILD AND DEPLOY MODELS INSIDE DB2
SOLUTION 2:

21.
ENTERPRISE AI

22.
SOLUTION 2 - DEMO

23.
ENTERPRISE AI
Analyzing Titanic disaster
Titanic disaster occurred 100 years ago on April 15, 1912, killing about 1500 passengers and crew
members. The fateful incident still compel the researchers and analysts to understand what can
have led to the survival of some passengers and demise of the others. With the use of machine
learning methods and a dataset consisting of 891 rows in the train set and 418 rows in the test set,
the research attempts to determine the correlation between factors such as age, sex, passenger
class, fare etc. to the chance of survival of the passengers.

24.
Connecting to db2
ENTERPRISE AI

25.
Model Training
DB2 – IDAX
framework
ENTERPRISE AI

26.
ENTERPRISE AI
In-db Inferencing
Benefits
– ML Infrastructure
– Low-latency
– High-throughput
– Simpler Integration

27.
ENTERPRISE AI

28.
OBJECT AUTOMATION SYSTEM SOLUTIONS PVT. LTD.

29.
ENTERPRISE AI
References
https://www.ibm.com/docs/en/db2/11.5?topic=content-in-database-machine-learning
https://www.dbisoftware.com/blog/db2nightshow.php?id=822
Db2 ML complete Masterclass
https://github.com/IBM/db2-
samples/blob/master/In_Db2_Machine_Learning/Building%20ML%20Models%20with%20Db2/
Notebooks/Classification_Demo.ipynb
https://www.kaggle.com/datasets/
https://gateway.on24.com/wcc/eh/2282867/category/41810/db2-aiml
Db2 Python UDF to operationalize Python ML pipeline
https://www.infoworld.com/article/3607762/8-databases-supporting-in-database-machine-
learning.html