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!

About Tanu: She is Top 30 Women in Product, YWCA Silicon Valley Tribute to Women and PMF with 20+ yrs of experience currently SVP of AI products in Seldon.io, HBS-trained technical product leader; ex-Autodesk, HPE, Oracle. She is an advisor with Techstars, IoT Tribe, and gov programs such as UK APPG AI, Singapore GovTech, and a Chairperson of the Board at Atlas Rosetta. About Seldon.io: Since 2014, Seldon has built enterprise infrastructure to deploy, manage, and scale ML with flexibility, observability, and cost-efficient modularity—so AI drives real business impact. Connect with Tanu: 🔗 LinkedIn: / tanuchellam 🌐 Atlas Rosetta: https://atlas-rosetta.com/ 🤝 EntorCruise (search “Tanu”): http://entorcruise.com/ Learn more about Seldon: 🌐 https://www.seldon.io/