All major cloud service providers now have some ML offering. The startup costs are low-to-no. They provide seamless leverage of cloud resources for scale-ups = $’s. Open source ML options are now common making the creation of very large models now possible. Open data sets are proliferating. Presented at CF Machine Learning

1. CFMLA Lot Has Happened
Feb 3rd 2016

2. WELCOME
Central Florida Machine Learning
(& Artificial Intelligence)
“Smart tech is here, now, and integral to your
engineering.”

3. INTROS

4. A Lot Has Happened
• 2015 “Year of Machine
Learning”
• Interest in AI is blazing
• Data Science is hottest
job
• Billions in research and
investment $$ committed
to AI&ML research

5. 2016
• CFML collaborations:
BigDataFlorida.org, Florida
Healthcare Innovators, Ft Lauderdale
Machine Learning, IOT groups
• 2016 = year of intelligent
applications, “smarter” product lines
everywhere
• Elon Musk sets aside $1BN for open
source AI
• AI backed IOT roll-outs across many
markets
• Python eclipses R as go-to language
for ML

6. MLaaSMachine Learning as a Service
Feb 3rd 2016

8. Machine Learning as-a-Service
(MLaaS)
• All major cloud service providers now have some
ML offering
• Startup costs are low-to-no
• Seamless leverage of cloud resources for scale-
ups = $’s
• Open source ML options are now common
• Very large models now possible
• Open data sets proliferating

9. Typical ML Cycle
Historical
source data
Predicted
outcomes
Incident
Data
Test data
Training data
80%
20%
Machine
Learning
Training
Machine
Learning
Training
Machine
Learning
Training
Machine
Learning Model
Machine
Learning Model
Machine
Learning Models
Model weights
Quality Metric
Predicted
outcomes

10. Test data
Training data
Historical
source data
Machine
Learning
Training
Machine
Learning Model
Model weights
Quality Metric
Predicted
outcomes
Incident
DataHistorical
source data
Upload
MLASS In The Cloud
Predicted
outcomes

11. • Open source software library
• Numerical computation using data
flow graphs
• Developed by the Google Brain
Team
• For students, researchers,
hobbyists, hackers, engineers,
developers, inventors and
innovators and is being open
sourced under the Apache 2.0 open
source license
• TensorFlow is not complete

12. • Nodes in the graph
represent mathematical
operations
• Edges represent the
multidimensional data
arrays (tensors)
communicated between
them
• Deploy computation to one
or more CPUs or GPUs in
a desktop, server, or
mobile device with a single
API
• TensorFlow Python or C++
programs, or iPython
notebook
Computer vision intro:
https://www.tensorflow.org/v
ersions/0.6.0/tutorials/mnist/b
eginners/index.html

13. • Interact with you data with
natural language and
speech, through Cortana
• State-of-the-art machine
learning algorithms
• Proactive alerting and
recommendations on actions
to take
• Face detection, identification
and verification
• Recognize objects within
images
• Speech recognition and
response
• Translate spoken words into
intent
• Analyze text
• Determine overall sentiment
and key phrases
• GitHub and to the MIT open source license. APIs and templates
from Solutions Gallery
• Data of any volume, variety and velocity both on premises and in
the cloud
• Deep learning
• R, Python and Hadoop
• Compliance standards such as ISO 27001, HIPAA, FedRAMP,
SOC 1 and SOC 2

14. • 2016 InfoWorld
Technology of the Year
recipient
• Data discovery service
• On the cloud
• Guided data exploration
• Automated predictive
analytics
• Dashboard and infographic
creation
• Social media analysis
• Loads of free online
webinars and tutorials

15. • Create Machine Learning
Models
• Amazon Machine Learning
APIs and wizards
• Data stored in Amazon Simple
Storage Service (Amazon S3),
Amazon Redshift, or MySQL
databases in Amazon
Relational Database Service
(Amazon RDS)
• Scalable ML algorithms
• Model visualization tools
• Quality alerts
• Amazon Machine Learning is a
managed service
• Scalable, High Performance
Prediction
• Batch prediction API, or use
the real-time API
• Low Cost and Efficient
• No setup cost and you pay as
you go
• Start small and scale as your
application grows.

17. • Set up a Source
• Create a Dataset
• Create a Model
• Generate Predictions
• Create Ensembles
• Evaluate Models or
Ensembles
• Try BigML as much as
you want. Tasks
under 16MB are
FREE using
development mode

18. • Python / Anaconda
• GraphLab Create is an
extensible machine
learning framework
• GraphLab Canvas
• iPython Notebooks
• SFrames
• Coursera Free / Trial

20. LIVE DEMO

21. LIVE DEMO

22. Karl Seiler | President
karl@piviting.com
Piviting.com
@pivitguru
SMARTER CHANGE