USAA was part of multi-company benchmarking survey with companies that started using machine learning capabilities with customer facing applications. We learned that there are four common practices and architectural changes that these companies engaged in that less successful companies did not.

1. Four Moves to Scalable
Machine Learning
Robert Welborn, USAA

2. Move from
monoliths to
distributed compute
• Builds the basis for access to tools
across the enterprise instead of on
appliances
• Point your analytic infrastructure at
operational data instead of analytic
data
• Transactional data drives your
marketing models

3. Move from Batch to
Real Time
• The latency isn’t the problem, the
batch philosophy is the problem
• Process when the information is
available, not on a schedule, the speed
will get better as your processes
improve
• Models that have worked for years will
need to be retooled (scoring can’t take
all night)

4. Move from Static
Coefficients to
Responsive
• Create the ability for fast feedback across
the enterprise
• The “learning loop” the model learns
from the results of the last experiment
• If you are in a regulated industry, you
have to bring your compliance folks along
for this journey
• Humans make decisions about what to
test not what to show whom

5. Move from on the
Premises to the Cloud
• Don’t reinvent scaling, scaling was designed
for the cloud, at best, you are trying to do
things better than people who already solved
the problem, at worst, you’re “scaling”
unsustainably
• Allocate the right tasks in the cloud and on
premises