From Amazon to Google, top technology firms have embraced data science and machine learning to improve business outcomes. Yet AI adoption beyond these firms has been slow due to obstacles such as hiring talent, heterogeneous data, and compute infrastructure. Larger firms have built teams to tackle these issues with some success, but small- and mid-tier firms are at a distinct disadvantage. AI as a Service is a paradigm that levels the playing field and empowers businesses across the spectrum.

1. ARTIFICIAL
INTELLIGENCE AS A
SERVICE
John Liu, Intelluron Corp

2. Fill in the blank
Data Science is:

3. Data Science
Answering business questions
through data.

4. Fill in the blank
Machine Learning is:

5. Machine Learning
Ability to improve performance
without explicit instruction.

6. Fill in the blank
Artificial Intelligence is:

7. Artificial Intelligence
Ability of a machine to replicate
intelligent human behavior.

8. Worldwide Spending on
Cognitive and Artificial
Intelligence Systems
Will Grow to $19.1
Billion in 2018
Source: IDC

9. 40% of digital
transformation
initiatives will use AI
services.
AI spending will grow to
$52.2 billion in 2021.
Source: IDC

10. Corporate Adoption
AI/ML Awareness
AI/ML Adoption
AI/ML Native

11. 3 big ideas…

12. #1 AI Adoption Should Focus on
Business Value

13. Why Analytics Projects Fail
• Lack of focus on business question/ROI
• Failing to define metrics and success criteria
• Data strategy: data availability, quality,
delivery, lineage, storage, access
• Failing to address ERPQ

14. Do’s and Don’ts
Don’t We need AI to stay competitive
(FOMO)
Do How can advanced analytics
improve operating margins

15. Source: WisdomSchema

16. Answering Business Questions
Descriptive
• Business Intelligence
• SQL
• OLAP
• Spreadsheets
Prescriptive
• Data Science
• ML
• AI

17. #2 Business Value is Only Achieved
when Models are in Production

18. Common Scenario
DS I’ve built a model that can … using
XLNet and a 1000-layer ResNet
model in tensorflow
IT Great, but it takes HOW LONG to
process a single document on a
1024 GPU cluster?!

19. ERPQ
Enterprise
Ready
Production
Quality

20. Data Science
Cycle
Credit: Aaron Lipeles

21. Some DS/ML ERPQ Challenges
Data Notebook/Code Model
Quality check
Access Security
Versioning
Lineage
Storage
Versioning
Documentation
Dependencies
Versioning
Reproducibility
Lineage
Documentation

22. Data Version Control (DVC)
https://dataversioncontrol.com/

23. CookieCutter
Data Science
https://drivendata.github.io/cookiecutter-data-science/

24. Pachyderm

25. MLflow
MLflow Tracking MLflow Projects MLflow Models
Logging:
• parameters,
• code versions
• metrics
• output files
in any environment
for later comparison.
Packaging reusable
data science code
(including
dependences) that
can be chained
together into
multistep workflows.
Standardized
packaging of ML
models for
deployment in
diverse distributed
platforms.

26. MLflow Integrations

27. Model Deployment: Containers
Source: Cisco

28. Container Ochestration

30. Kubeflow
Source: Microsoft

31. #3 Leverage Consulting Resources
to Bootstrap whenever possible

32. Deployment
• Data strategy
• Data science
• Model training
• Prod Infrastructure
• Prod Monitoring
• Model drift/retrain
• Biz Value review

33. AI as a Service
• Built-in Data Storage/Versioning/Lineage
• Platform for Model Building & Tuning
• Production-ready Deployment & Integration
• Secure and Private platform
• Cloud Service – Pay as you go

34. Consultants with Expertise
• Data Strategy
• Open Source AI/ML tools
• Cloud Infrastructure
• Serverless Architecture
• Security

35. Last Thoughts
• Training Data vs Training Examples
– Transfer Learning
– Domain Adaptation
– Few Shot Learning
• Machine Learning with Small Data
– Automating Data Science with AI!

36. Thank you.