Productionizing AI involves not only building AI-powered services but also testing, monitoring, and refining them to enhance quality throughout their lifecycle. Unlike traditional software, AI services require distinct management practices to meet standards and best practices. IT teams need solutions that help them to build and maintain AI services at every stage of their lifecycle. The AI services lifecycle has several stages: design, development and integrations, quality assurance, running and operating, governance, security and scale, and feedback and analysis. Businesses need to keep in mind this lifecycle to maximize the value and benefits of production-grade AI.
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