Shirsha currently works in Thomson Reuters Labs leading the Engineering team in Bangalore. In her current work she is responsible for successful delivery of AI ML projects for internal platforms, tools and products. Having experienced the ML lifecycle and the tangent that it has to the software development lifecycle, she will share on machine learning operations and what it takes to land a solution with ML!
In her prior avatar she has worked in Mercedes Benz R&D India, Nokia and other companies, on building and deploying solutions with Big Data, Analytics and ML.
She started her career as a software engineer building solutions for handsets in Motorola, moving onto 3G call processing software, network probes. It was a gig on data processing for location based services that immersed her in the world of Big Data, Analytics, pivoting her career to the ML space now.
She holds her experience as a software engineer special and hopes to see the level of standardisation in ML projects as exists in regular software projects.
CRITICAL OBSERVABILITY FOR TESTING AI SOLUTIONS
Observability in AI involves understanding how inputs translate into outputs. It’s critical for testing AI solutions and faces challenges like complex models and vast data. Metrics like error rates measure observability. Various testing methods and tools can enhance it. Illustrative case studies, a look at future trends, ethical implications, and best practices in AI observability provide a comprehensive perspective.