Bridging the gap in enterprise AI - we give a quick three-step introduction to the topic: 1) How to take a down to earth approach to enterprise AI 2) What gaps are there that your company needs to tackle? 3) We introduce one bridge from a zoo of tools: SKIL. In particular, we talk about recent development in SKIL's Python client, enabling Data Scientists

1. Max Pumperla Version 1.0
Bridging the gap in
enterprise AI

2. Enterprise AI
The Gap
A Bridge

3. Skymind overview
● Deep learning for enterprise
● Globally distributed, remote
● Creators of Deeplearning4j (DL4J)
● 300k+ downloads per month

4. ● A bit too busy on GitHub
● DL4J & Keras core dev team
● Author of elephas
● Author of betago
● Hyperopt maintainer
● Coursera instructor
● Author of “Deep Learning and the
Game of Go”
● ...
About me

5. AI time-to-value is a big problem today
The right tooling can speed up the lifecycle
Building models is not the problem
Bold claims for kick-off

6. Enterprise AI
“The question of whether machines can think is about as
relevant as the question of whether submarines can swim.”
- E. Dijkstra

7. A paradigm shift at work
Data
Algorithm
Output
Data
Output
Algorithm
vs

8. What are the drivers?
● Data
● Hardware & Compute
● Theoretical advances
● Software

9. Think Software 2.0*

10. Stages of Enterprise AI
● Digitized, not able to leverage AI
● AI ready, cloud APIs only
● AI ready, custom solutions
● Mastered AI (AGFA)

11. The Gap
Machine Learning: The High Interest Credit Card of
Technical Debt

12. Political
● Strategic alignment (e.g. DS vs. Product)
● Resource allocation
● Wrong expectations
● Not willing to pay the price (talent, infrastructure,...)
Cold start

13. Cultural / Team
● Separation of “Science” and “Engineering”
● Data scientist lack realistic view
● Cool stuff never gets used
● Data scientists don’t want to do the dirty parts
● Experiments faster than product integration
● Lack of expertise
● Difficult to hire
Model hand over

14. Software design
● Unintended coupling
● Glue code everywhere
● Difficult to fix once broken
The Homer

15. Data & Infrastructure
● Separate infrastructures
● Insufficient structure
● Difficult access
● Inconsistencies and errors
● Data wrangling biggest part of the equation
Pipeline jungles

16. Production & Monitoring
● Model needs to go to production
● Models needs to stay there, up-to-date
● Often clumsy process
● Track model performance & react
● Track input & output data
● Deal with consequences
● Communicate results
Keeping logs

17. Modelling risks
● World changes, model can’t catch it
● Training data not realistic
● Model does not generalize
● When to retrain, update, or fallback?
● When to update test set?
Poor live performance

18. Tools
anyone?
Pick!

19. Data Science Data Engineering
A Bridge

20. Integrated AI platform
SKIL

21. SKIL Python interface
● End-to-end model lifecycle management
● Works anywhere, carry on
● Captures your workflow:
○ Define a workspace for your experiments
○ Create tracked experiments
○ Add models to your experiment
○ Add evaluation metrics
○ Create deployments for your model
○ Deploy models as services
○ Get predictions from services

22. Model deployment example
skil_server = Skil()
work_space = WorkSpace(skil_server)
experiment = Experiment(work_space)
model = Model('model.h5', experiment)
deployment = Deployment(skil_server, "my_deployment")
service = model.deploy(deployment, scale=2)
print(service.predict(test_data))
pip install skil*

23. Use case:
Distributed training on Spark

24. Conclusion
● What is your view on enterprise AI?
● Identify your gap
● Bridge it accordingly
DS meetup Dec 13th:
live demo of SKIL for object
detection with YOLO

25. Thank you! github.com/maxpumperla
twitter.com/maxpumperla