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Deploying Machine Learning Models to Production
Machine learning allows businesses to
operate efficiently at scale and glean
transformative insights from data. The
field is fast-evolving with new advances
almost every day, yet translating
state-of-the-art models into stable
software can be unexpectedly
challenging.
In this brief, we’ll walk through the critical decisions and common pitfalls in building and deploying production-grade machine learning software. We hope this will help your organization make the most of your machine learning tools – to serve more customers, help more people, and make more high-impact discoveries.
Lessons learned from cross-industry experience
At Strong Analytics, we have worked
closely with companies of all sizes to
design and implement powerful data
science pipelines tailored to each client’s
unique position and needs. Having
deployed numerous business-critical
models serving billions of requests in
contexts requiring both high accuracy
and near-instant response times, we’ve
seen first-hand how machine learning can
transform the way an organization
operates and accelerate its growth.
We work with top brands in a range of industries.
