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AI Frontier Network
Curated analysis and practitioner signal from teams deploying AI in production.
Recent episodes and related editorial outputs.
Structured perspectives from operators and decision-makers.
Vivek Pandit
Founding MLE
I believe we need to first understand what's the utility of evaluations. Evaluations as a tool for quantitative benchmarking and setting a common source of truth that people can agree on is really important to establish. This helps set a...
Praveen Kumar Koppanati
QA Automation Lead
When benchmarks stop reflecting reality, the first thing I remind myself is that benchmarks are not “wrong”, they’re just safe. They’re clean, stable and predictable. Production is none of those things. In the real world, data shifts, us...
Deepak Dasaratha Rao
Benchmarks stop being useful the moment they become “clean-room exams”: static data, stable labels, and a single notion of success. In production, you care about outcomes, risk, cost, and experience. Decision quality lift over baseline (...
Ram Kumar Nimmakayala
Principal Product Manager, AI at Western Governors University
Three Shifts That Distinguish Scale from Experiments: Center of Excellence versus Federated Enablement: Central AI organizations lead to innovation bottlenecks. The hub-and-spoke model – where platform teams are responsible for building...
Laxmi Vanam
Data Strategist and Advanced Analytics Lead
Scaling AI requires far more than replicating successful pilots. What changes at scale is not the model, but the operating system around it. As AI moves from one team to many, organizations must standardize data foundations, decision own...
Anshul Garg
Product Leader @ Amazon
Moving AI from one team to many isn't a deployment problem, it's a people problem. The Operating Model Shift When AI lives in one team, you can get by with informal handoffs and tribal knowledge. Scale that to ten teams, and everything b...
