MakinaRocks Runway™ MLOps platform enables anomaly detection for semiconductor laser drills with one month advance warning
Manufacturing AI projects have a success rate of only nine percent due to the wide diversity of manufacturing environments and data, forcing each manufacturer to have a customized model that then fails when deployed into unpredictably different production conditions.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Laser drill sensor data collected
The client company operated dozens of laser drills, each with different core parts and parameters producing a unique distribution of data.
Tools used
Runway™Link™Jupyter
Outcome
For a tech-sector client, Runway™ cut deployment time from six months to four weeks (reduction of about 80%) and reduced MLOps development manpower by about 50%. For the semiconductor laser drill client, the solution detected anomaly signs one month in advance and maintained monitoring through sensor failures. General users can now retrain models without data scientists or ML engineers.
What failed first
Previous AI models for the laser drill client could not produce inference results when some sensors failed, causing frequent monitoring interruptions and client dissatisfaction.