Delivered at CF Summit Europe 2016 in Frankfurt, 28th September 2016
The first part of this talk argues why Cloud Foundry is ideal for operationalising and scaling machine learning models.
The core of the talk describes a scalable cloud native architecture for operationalising data science models based on microservices. There is a video demo of this architecture in action.
The last part of the talk explains the limits of Cloud Foundry in the context of data science and what still has to be achieved for Cloud Foundry to become an end-to-end data science platform.
Data Science on CF
Who am I?
● Data Scientist working with clients at Pivotal Labs
● Cloud Foundry user
● Community Buildpack writer
Everybody wants systems that
are smarter, everybody wants
systems that are more predictive,
everybody wants everything
scored, everybody wants to
understand what’s the next best
offer, next best opportunity,
how to make things a little bit
Forbes CIO Summit, March 7, 2016
What’s the problem?
If your Machine Learning model is not in production,
it does not provide business value.
A slide deck does not count as production!
Who has this problem?
Data Scientists Want their model to make
Developers Want to add ML to their app
CIOs/CDOs Want return on ‘Big Data’
Our Clients Want to implement their
first ML models
Day 1 Problems
Load and Transform Data
Train the Predictive Model
Connect to Incoming Data
Run the Model
‘Scoring As A Service’
‘CF powered Learning’
in CF or
In-Stream (Online) Learning
How can I do this?
Spring Cloud Data Flow
Marketplace data services
Python ML microservices
Initial offerings from GE, IBM, Alpine, Bosch
Day 2 Problems
Which predictions were made with the old model or new?
Do I need to continue serving the old predictions?
Which library versions were used with old model and new?
Has the data schema changed in the underlying system?
Can I provide the right inputs for the different model versions?
Can I replay the stream?
Update the Model!
Using a Model Service
● Version control for model
● Parse data with varying schemas
● Serve appropriate model version
based on consuming app
● Store underlying data for model
re-training and reproducibility
What’s Next for Data Science on CF?
More data services
More demos of data science/ML models
Examples of successful projects
Building blocks for building your own ML services