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
•  ...