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Redis Labs commissioned Forrester Consulting to survey IT decision makers responsible for ML/AI operations strategy. The study reveals that IT decision makers believe their current data architectures won’t meet future model inferencing challenges.
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Redis Labs commissioned Forrester Consulting to survey IT decision makers responsible for ML/AI operations strategy. The study reveals that IT decision makers believe their current data architectures won’t meet future model inferencing challenges. There is a need for a modern AI infrastructure that can accelerate the ML lifecycle, improve interaction between data scientists and ML engineers, and above all, improve accuracy of ML.
Decision-makers are going all in with ML to create AI apps, but critical hurdles keep them from their desired transformation. Over 40% agree their architecture is not good enough for the future.
The high demand for real-time model inferencing (using ML models in production) exposes major challenges with accuracy, latency, and reliability in current architectures.
Running ML model inferencing in-database where data is stored solves some critical challenges.
Greater uncertainty and unforeseen disruptions impacting business continuity
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