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In the race to deploy AI, we often focus on the "science" while neglecting the "pipes." The result? Silent failures, broken dashboards, and data scientists spending 80% of their time fixing upstream schema changes. This session moves beyond the AI hype to address the engineering reality: How do we build systems that don't break? Drawing on a decade of experience in Telecom and Industrial AI, I will introduce the concept of Data Contracts—API-like agreements between data producers and consumers. We will explore how to treat "Data as a Product" by implementing automated enforcement and quality guards within a Lakehouse architecture. The "Why": Understanding the hidden cost of silent data failures in ML pipelines. The "How": A practical framework for defining YAML-based contracts to enforce schemas and data freshness. The Tooling: A look at how to leverage PySpark and Delta Lake to build "circuit breakers" for your data. The Strategy: How to shift your team's culture from reactive firefighting to proactive quality engineering.
In the race to deploy AI, we often focus on the "science" while neglecting the "pipes." The result? Silent failures, broken dashboards, and data scientists spending 80% of their time fixing upstream schema changes. This session moves beyond the AI hype to address the engineering reality: How do we build systems that don't break? Drawing on a decade of experience in Telecom and Industrial AI, I will introduce the concept of Data Contracts—API-like agreements between data producers and consumers. We will explore how to treat "Data as a Product" by implementing automated enforcement and quality guards within a Lakehouse architecture. The "Why": Understanding the hidden cost of silent data failures in ML pipelines. The "How": A practical framework for defining YAML-based contracts to enforce schemas and data freshness. The Tooling: A look at how to leverage PySpark and Delta Lake to build "circuit breakers" for your data. The Strategy: How to shift your team's culture from reactive firefighting to proactive quality engineering.
I am a Senior Data Scientist at Cummins Inc., where I lead global teams in deploying high-stakes AI solutions in the area of Manufacturing, Prognostics and Health Safety & Environment. My journey in data began in the telecom sector, where I spent over three years as a Data Analytics Engineer mastering the complexities of high-volume network data. Today, I specialize in bridging the gap between digital intelligence and physical manufacturing.
A seasoned speaker who has presented on Physical AI at the Great Lakes Analytics and AI Summit, I am a vocal advocate for 'Data as a Product.' As an inventor with a pending patent in predictive maintenance, I focus on the engineering rigor and data contracts required to build resilient, self-healing pipelines within Lakehouse architectures. My goal is to ensure AI remains reliable from the sensor to the boardroom. I hold an M.S. in Computer Science from and am deeply committed to increasing the visibility of women leading deep technical conversations in AI.
