Case Study: Trigo streamlines ML workflows and accelerates experimentation with ClearML

Trigo Takes a Successful Shopping Trip for ML-Ops

Trigo, a computer vision startup reshaping retail checkout, needed a way to manage a complex AI/deep learning lifecycle with the same discipline as software engineering. The team wanted smoother research-to-production handoff, better versioning and collaboration, and less overhead from juggling multiple tools and DevOps processes, so they turned to ClearML and its MLOps platform alongside PyTorch.

With ClearML, Trigo integrated experiment management and orchestration directly into its code with only a small snippet, enabling data scientists to clone experiments, schedule runs on on-prem GPUs, and manage models without repackaging code or waiting on DevOps. ClearML helped Trigo streamline workflows, speed up iterations, and support CI-style testing on blind datasets before merging models, saving researchers substantial time throughout the workday and improving overall ML efficiency.

Trigo