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When our materials characterization lab needed to analyze 10,000+ microscopy images per day, manual inspection was creating a 3-week bottleneck. I led a team to build a production computer vision system that reduced analysis time from weeks to minutes while maintaining research-grade accuracy.

When researchers at our materials science division needed to extract insights from thousands of scientific papers, our traditional keyword search was returning noise instead of knowledge. I rebuilt the entire research workflow using production-grade RAG pipelines that transformed how scientists interact with literature.

When manufacturing equipment starts failing, you have minutes (not hours) to catch it before it causes expensive damage. Our batch processing system was generating alerts 6 hours too late, so I rebuilt the entire pipeline to process IoT sensor data in real-time.

Healthcare data is the holy grail for ML models, but it’s also the most legally terrifying data to work with. After months of lawyers telling us “you can’t do that” and “HIPAA violations cost millions,” I finally found a way to train models on patient data without actually seeing the data.

Our ML training jobs were getting ridiculous. 2-week training runs on single GPUs, models that couldn’t fit in memory, and constant hardware failures wiping out days of progress. I knew we needed distributed training, but getting it right in production was way harder than the tutorials made it look.

SAM looked incredible in the research papers, but getting it to work reliably in production was a different story entirely. After weeks of wrestling with model sizes, inference times, and memory issues, I finally got it running smoothly for our microscopy analysis pipeline.

When your CEO asks why conversion rates dropped 15% overnight and you’re staring at yesterday’s batch reports, you realize real-time analytics isn’t a luxury - it’s survival. After our third “we need this data NOW” emergency, I convinced leadership to let me rebuild our analytics pipeline from scratch.

Our AWS bill was getting ridiculous. $50K/month in GPU costs with clusters sitting mostly idle - classic. After getting some raised eyebrows from finance (and a few pointed questions about “why we’re paying for GPUs to watch Netflix”), I had to figure out how to actually use what we were paying for.