Feb 26
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Why do nearly 90% of machine learning projects never reach production? In this brief breakdown, we explore the critical gap between model development and real-world deployment. Discover the MLOps best practices—including Continuous Integration (CI), Continuous Deployment (CD), and Model Monitoring—that transform experimental models into reliable business solutions. We also touch on the "Infrastructure Reckoning": why enterprises are shifting to Local LLMs for predictable Total Cost of Ownership (TCO) and Edge AI for ultra-low latency. Whether you are integrating AI with legacy systems or building a Center of Excellence (CoE), this video provides the foundational roadmap for scaling AI effectively. 🎧 WANT THE FULL DEEP DIVE? Listen to the podcast episode Follow Neural Intel: 🐦 X/Twitter: @neuralintelorg 🌐 Website: neuralintel.org