AI / Machine Learning Industry Case Study
Taking AI Models Out of Code Notebooks and Into Production. Automating data pipelines so machine learning engineers can deploy updates without waiting on DevOps support. Python Docker GitHub Actions AWS FastAPI +more
Clarix had brilliant data scientists building great predictive models. However, moving those models out of experimental scratchpads and into a live, customer-facing cloud app was slow, manual, and prone to breaking.
Clarix had models that worked in notebooks and broke in production. We built the MLOps layer around them — versioning, monitoring, rollback, and a deployment pipeline that the data science team could own without DevOps help.
Our Solution
We built a clean, automated container pipeline around their models. We set up automated checks, resource isolation, and quick rollback scripts, giving their data team total control to push updates safely.
Topmost ROIs for their business
on top of getting a 3x faster model deployment after pipeline setup
Increased model update velocity by 3x, cutting deployment down to minutes.
Eliminated system downtime caused by heavy memory load crashes.
Saved hours of engineering time previously wasted on manual cloud setups.
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These are not small problems. They get worse the longer they sit. Book a free call and we will tell you exactly what needs to change and what stays
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