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Deploy ML Models to In-Memory Databases for Blazing Fast Performance

ANALYST STUDY

A Forrester Consulting opportunity snapshot, sponsored by Redis

Redis 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.  

There are several components to this modern AI infrastructure, and Redis plays a key role in the low-latency storage and serving of online features. 

Some of the key insights from this study include:

  • 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.

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Highlights

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Source: “Deploy ML Models To In-Memory Databases For Blazing Fast Performance,” a Forrester Consulting opportunity snapshot commissioned by Redis, June 2021

Redis as an online feature store

Feature store is becoming an important component in any ML/AI architecture today. A feature store allows you to build and manage features for your machine learning training phase (offline feature store) and inference phase (online feature store) to ensure the highest model quality. In this RedisConf 2021 session, you will learn about the importance of the feature store in your ML/AI stack, and how you can better leverage Redis and Redis Enterprise to improve the quality of your models and accelerate your AI/ML inferencing.

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