Slalom Accelerates Model Development and Cuts Setup Time Using Lightning AI

Slalom’s small Executive Data Team used Lightning AI to build and operate a sales-opportunity forecasting system despite having only one full-time machine learning engineer and limited infrastructure support. The team needed to backtest models against years of sales data, run large-scale experiments, and evaluate confidence across scenarios, which required scaling from dozens to hundreds of machines. By deploying sandboxed AWS-based Lightning Studios and self-hosted ClearML for experiment tracking, the team ran hundreds of thousands of experiments, reached up to 140 simultaneous tasks, and deployed a validated model into a daily Salesforce prediction pipeline. Lightning reportedly saved an estimated two to three months of infrastructure setup time, reduced environment and permissions work from much of a day to about two hours, and enabled contributors to reuse shared, reproducible workspaces with minimal onboarding. With its tabular ML workflow established, the team is now prototyping LLM-based applications and using Lightning’s collaborative environments and pipelines to support faster experimentation, handoffs, and production-oriented AI development.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.