Do you know 3 out of 5 ML / AI engagement do not go to the production or cant operate in a enterprise production eco system. It is important to have Security, Salablity, Auditing, Alerting system, Version controlling and flexible deployment in place when we build an Artificial Intelligence solution to deploy in production. By using Microsoft AI - We ll learn how to make this work. Without all these features going to production ll be a nightmare. In this session we ll discuss about - what it takes to be production ready and how to avoid those pitfalls.

2. 70%
30%
Across Europe
Models runing in production Still figuring out

4. 50%
30%
20%
Across Europe
Not ready for production Still struggling Production ready

5. Accessibility
70% of AI users ll not be technical

6. Machine Learning
Artificial Intelligence
RPA
BOT
APP
EDGE
WEB
VA
ERP
3rd
Party

7. Define Compute
Create & Run
Web Service
Docker Image
Data Model
Request (URI, KEY)
Train
Response
Test
Develop
Consume
Update
Deploy
IOT EDGE
DEVICES

8. Elasticity

9. Azure Kubernetes Cluster
NODE NODE NODE
Horizontal POD Auto-Scaler
NODE NODE NODE NODE NODE
Scale Out

10. Elasticity

11. Security

12. Define Compute
Create & Run
Web Service
Docker Image
Data Model
Request (URI, KEY)
Train
Response
Test
Develop
Consume
Update
Deploy
IOT EDGE
DEVICES

14. Diagnostics

15. Web Service
Authentication
Load
Compute Cluster (AKS)
Cluster Health
Data & Model
Model Data Drift
Data Snapshot

18. Pipeline & CI/CD

20. Alert

22. Portability

24. ML / AI
Engineer
Software
Developer

25. Build pipeline

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ashok.samal@microsoft.com