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Enterprise AI using IBM DB2
- 2. ENTERPRISE AI OBJECT AUTOMATIONSYSTEM SOLUTIONS PVT. LTD
- 3. 3 1 2 3 AUTOMATOR DECIDER RECOMMENDERILLUMINATOR 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 thebenefits 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 AIat 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
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- 9. ENTERPRISE AI Regulated Data DataVolume Noisy Data ML Development Challenges
- 10. ENTERPRISE AI Hosting Speed Integration ML Deployment Challenges 51% AIprojects don’t go beyond experiments
- 11. ENTERPRISE AI IBM –DB2 supports In-Database Machine Learning
- 12. Latency sensitive Decisions Large BatchPredictions 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 andOptimizing AI lifecycle with IBM DB2 01 02 Integrating Open Source models with DB2 Developing and Deploying DB2-Native ML models
- 14. BRING YOUR OPEN-SOURCEMODELS 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
- 16. SOLUTION 1 -DEMO
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- 18. Connecting to IBMPower9 system (Vina in university of Oregon) ENTERPRISE AI
- 19. Testing theDb2 setup
- 20. Importing necessary Librariesin Jupyter notebook ENTERPRISE AI
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- 23. BUILD AND DEPLOYMODELS INSIDE DB2 SOLUTION 2:
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- 25. SOLUTION 2 -DEMO
- 26. ENTERPRISE AI Analyzing Titanicdisaster 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.
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- 28. Model Training DB2 –IDAX framework ENTERPRISE AI
- 29. ENTERPRISE AI In-db Inferencing Benefits –ML Infrastructure – Low-latency – High-throughput – Simpler Integration
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- 31. OBJECT AUTOMATION SYSTEMSOLUTIONS PVT. LTD.
- 32. 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 MLcomplete 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