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AI/ML is now mission-critical for any growing organization, yet taking models from notebooks to production without accumulating technical debt remains a major challenge. At the same time, open source tooling play a vital role towards achieving fully automation. This session examines why many ML/DS systems fail in production and how a well-designed <strong>open-source MLOps stack</strong> enables scalable, reliable, and fully automated AI/ML platforms.<br><br>We’ll dive into the what and how of this practical MLOps architecture, covering model development, CI/CD, deployment, monitoring, and lifecycle management while highlighting key infrastructure and platform design choices required to support ML services at any scale. The session concludes with a <strong>hands-on demo showcasing true end-to-end automation using open-source MLOps tooling</strong>, eliminating platform friction and enabling production-ready ML from day one.
