SCALING AI SOLUTIONS: A PRACTICAL GUIDE TO PRODUCTION DEPLOYMENT
Keywords:
AI Deployment, Data Engineering, Cloud Integration, MLOps, Academic-Industry GapAbstract
This article examines the critical aspects of scaling artificial intelligence solutions from prototype to production environments, addressing the significant challenges organizations face during deployment. It explores key components essential for successful AI implementation, including robust data engineering practices, cloud integration strategies, and model performance maintenance frameworks. The article highlights the importance of structured deployment approaches, emphasizing how organizations implementing comprehensive frameworks achieve substantially higher success rates compared to those using ad-hoc methods. The article also addresses the academic-industry gap in AI education and provides practical guidelines for bridging this divide through hands-on experience and industry-aligned curricula. Through analysis of multiple case studies and research findings, this article offers actionable insights and best practices for organizations seeking to successfully transition their AI initiatives from development to production environments.
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