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5 Key Architectural Choices That Can Determine the Success of Your AI Product – Insights from Knowing Better IT

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🚀 Unveiling AI’s Real Challenges: Lessons from a Costly Black Friday 🚀

Last Black Friday, a seemingly minor flaw in a client’s AI pricing engine exploded into a $20,000 cloud overage in just 48 hours. This incident highlights a crucial truth in AI deployment:

  • Architecture Matters: Flaws in design amplify during production.
  • Failures Aren’t Always Obvious: Even well-performing models can lead to disaster if the underlying systems are weak.

In this article, we cut through the hype to uncover what truly determines the survival of AI products. Drawing from hard-earned post-mortem lessons, we reflect on:

  • Data bottlenecks
  • Latency spikes
  • The financial repercussions of poor design choices

For AI enthusiasts and tech professionals alike, understanding these factors is essential for navigating the complexities of machine learning in real-world applications.

🔗 Join the conversation! Share your thoughts and experiences with AI architecture below.

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