What she works on

• Machine Learning in production environments

• MLOps, model serving, monitoring & governance

• High-scale inference pipelines & feature stores

• A/B testing, model evaluation & drift detection

• AI platform architecture for enterprise teams

About

Lisa has built ML infrastructures powering real-time recommendations, fraud detection, and generative AI workloads at global scale. She has authored influential papers on MLOps strategy and production ML best practices, with work cited across the industry.

Her research on reducing model serving cost while improving latency has saved organizations millions in compute overhead. Lisa is passionate about democratizing machine learning in teams and teaches ML systems design — her educational content has been viewed over 100,000+ times worldwide.

Why attendees value her sessions

• Clear deep-tech explanations without academic fluff

• Real deployment challenges & how to solve them

• Proven frameworks for scaling ML workflows