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inaccessible or creates too much of a performance hit for scoring. As challenging as it
can be, it is a critical process to master in the
overall work flow.
Deployment Implications: Without systematically repeating data preparation for
AI models, prediction will simply fail. In
the worst case, such models will undermine
their core purpose of surfacing ranks, alerts
or recommendations. Effective decisioning
moments will be absent due to inaccurate
calculations and model outcomes.
Similar to Do #4 on model management,
the most important takeaway is build these
mechanisms for automated data transformations, flows and management in from the
onset. To do otherwise will set your AI initiative up for failure.
Don’t #3: Overlook full integration
Rationale: This is a premiere hang-up in
deployment. Many of our introductory discovery meetings sound like this: “We built
a model, but it won’t work in production.
We’re not worried about the methodology
or algorithms we used; we’re just stuck getting the operations team to implement our
logic.” Sound familiar? This is also known
as the great I T/OT divide.
We find that successful deployments oc-
cur when both business owners and technical
teams are at the table. Sounds naïve or overly
obvious? Perhaps, but when all parties help
one another think through the “what” and
“how” of using data science in their environ-
ments, deployments are far smoother. One of
our colleagues encouraged this simple point:
“Think through the labor required to inte-
grate models in settings outside of where they
were built.” This brings up the need for a flex-
ible platform that supports multiple languages
and the ability to avoid re-writing logic (often
thousands of lines long!). Can an R-based
scoring code be read into a Teradata database?
Look for integration points that help your IT
teams avoid days of testing their new code that
replicates what your data scientists authored.
Related to this, integration implies
knowing where the model will live and
execute. Can the performance of a REST
API actually render 10,000 SKU forecast
outputs down to a warehouse manager’s
mobile device? If not, the multi-SKU
forecast might as well not exist. Without
deployment, no data-driven decisions occur. In this case, stocking the wrong parts
could result in rush shipments for high-demand parts and excess inventory write-offs of seasonal parts.
Deployment Implications: Technology
can be your best friend or worst enemy. After
teams find AI solutions for anticipating profitable outcomes, they must embrace the holistic IT/OT process for production deployment to realize the predictive power observed
in their development environment. Making
your AI work in the field is greatly enabled by
agnostic technologies that can be understood
and manipulated by others in your company.
As a final point, think through where
the AI will physically execute and be relied
upon. If you don’t, this last mile of AI can
undermine millions of investment dollars
and opportunity costs.
Speed kills. Competitively speaking, the
adoption and use of new or refined technology is a differentiator for those who must
make complex, data-driven decisions. With
the broad promises of what Industry 4.0 and
the Industrial Internet of Things allude to for
manufacturers, one aspect is assured: applying AI to some very basic operations is doable now. Should you wait? We think not and
encourage a critical review of what we’ve seen
work in the field. M
Feature/ The Do’s and Don’ts for Deploying AI 10/10
The authors would like
to acknowledge the
following SAS Institute
colleagues for their contribution to this article
based on their professional field experience
and industry perspectives:
Kirk Chinavare, Senior
Nate Cox, Senior Associate Systems Engineer
David Duling, Director,
Advanced Analytics R&D
Gene Grabowski, Principal Solutions Architect
Abel Henson, Principal
Jesse Lund, Senior Associate Systems Engineer
Diana Shaw, Manager of
Americas AI Team, Global
Wayne Thompson, Chief
Data Scientist & Senior
Manager, Product Management
David Ungaro, Principal
Varun Valsaraj, Senior