Report

Use Real-Time Databases for Machine Learning Models, Forrester Survey Finds

A Forrester Consulting opportunity snapshot, sponsored by Redis Labs

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.

What you'll learn

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.

Key insights:

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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