Why most AI projects fail in production (and how Amazon solves it)
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Data Engineering, Machine Learning, AI, Data Trust, AI Governance, and ML Ops are transforming how companies scale intelligent systems. In this episode of Builders, Deepak Yadav, Engineering Leader at Amazon with 19+ years of experience in data engineering, analytics, and AI/ML, shares what it really takes to move machine learning from proof of concept to production. We explore the hidden challenges behind operationalizing AI, building trusted data platforms, implementing AI governance at scale, and creating systems that organizations can rely on. Deepak also discusses how AI is reshaping data engineering, the future of automation, and emerging trends like decision intelligence, synthetic data, and self-healing systems. Whether you're a tech executive, AI leader, VP of Engineering, CTO, Data Engineer, or ML engineer, this conversation offers practical insights into scaling AI responsibly and effectively. Topics covered: • Data Engineering and AI at scale • Machine Learning production challenges • Data Trust and AI Governance • ML Ops best practices • AI adoption in enterprises • Hiring and scaling data teams • Startups vs. enterprises in AI • Decision intelligence and synthetic data • Future AI trends and automation #DataEngineering #MachineLearning #AI #MLOps #DataTrust #AIGovernance #DataPlatforms #DataScience #AITech #BuildersPodcast
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