https://ryanorban.com/categories/model-deployment/Recent content in Model-Deployment on Ryan OrbanHugoen-usme@ryanorban.com (Ryan Orban)me@ryanorban.com (Ryan Orban)Ryan OrbanMon, 06 Dec 2021 00:00:00 +0000https://ryanorban.com/notes/forrester-operationalizing-machine-learning/Mon, 06 Dec 2021 00:00:00 +0000me@ryanorban.com (Ryan Orban)https://ryanorban.com/notes/forrester-operationalizing-machine-learning/<h3 id="summary" class="scroll-mt-8 group"> Summary <a href="#summary" class="no-underline hidden opacity-50 hover:opacity-100 !text-inherit group-hover:inline-block" aria-hidden="true" title="Link to this heading" tabindex="-1"> <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="none" stroke="currentColor" stroke-linecap="round" stroke-linejoin="round" stroke-width="2" class="lucide lucide-link w-4 h-4 block" viewBox="0 0 24 24" > <path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71" /> <path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71" /> </svg> </a> </h3> <p>This Forrester Research report (likely 2021, consistent with the save date) surveys the state of enterprise machine learning operationalization — the set of processes, tools, and organizational structures required to reliably deploy ML models into production and keep them performing well. The core finding is that most organizations are better at building ML models than at running them: data science teams can produce interesting prototypes, but the handoff to engineering for production deployment, monitoring, and maintenance is a persistent source of failure.</p>