Don’t Ship Broken AI: A Product Playbook for Reliable Machine Learning

Episode notes

Why do so many AI projects stall in notebooks - and what actually gets models into real-time production with measurable ROI? In this episode, Tanu Chellam, SVP of product at Seldon.io breaks down the no-BS path to deploy, monitor, and scale ML at enterprise level.. You’ll learn: The shift from “chatbot = AI” to real MLOps and why that matters The true cost of latency and late model releases (FS, e-commerce, manufacturing, healthcare) Why models get stuck in notebooks - and the playbook to ship anyway MLOps maturity signals: team, skills (Kubernetes/infra + ML), and tooling Human-in-the-loop vs. person-in-the-middle (and when to use which) Model monitoring 101 for execs: uptime/SLA vs. drift, bias, outliers, explainability Hiring sequence: DevOps→Data Science (reuse models) vs Data Science→DevOps (build from scratch) How to “win enterprise AI”: champions, partnerships, evolving product-market fit 3 enterprise AI wins right now: fraud detection, recommendation engines, expert AI agents for support A quick learning point not mentioned in the chat: "Human In The Loop" is an AI concept and "Man In The Middle" is a cyber security term!

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