ML Ops Era
I/T focus on Data Science and ML Ops but manually “wire” AI into solutions for point-to-point isolated successes. Successes are not repeatable.
I/T focus on Data Science and ML Ops but manually “wire” AI into solutions for point-to-point isolated successes. Successes are not repeatable.
Business and I/T work together leveraging a platform deploying repeatable patterns for AI solution success and measurable business impact.
Enterprises unleash sustained value and client trust by knowing and serving them better and through the right channel.
Carnegie Mellon's Software Engineering Institute defines AI Engineering as ". . . an emergent discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts."
~ Carnegie Mellon SEI
Gartner says "A robust AI engineering strategy will facilitate the performance, scalability, interpretability and reliability of AI models while delivering the full value of AI investments.”
~ Gartner
Gartner predicts "By 2025, the 10% of enterprises who establish AI Engineering best practices will generate at least three times more value with their AI efforts than the 90% of enterprises who do not."
~ Gartner
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