/plushcap/analysis/tecton/tecton-combining-online-stores-for-real-time-serving

Combining Online Stores for Real-Time Serving

What's this blog post about?

Tecton supports using DynamoDB or Redis as an online store for machine learning models in production to retrieve features for real-time inference. The choice between the two depends on a mixture of cost, performance, and operational overhead considerations. For models requiring low latency, Redis is recommended due to its lower latency and better tail latencies compared to DynamoDB. However, for models requiring high query volumes, DynamoDB's autoscaling feature may be more cost-effective than adding additional Redis nodes. On the other hand, for models requesting features from large datasets, DynamoDB is a better option due to its ability to handle larger dataset sizes compared to Redis. Additionally, if a model needs to be online immediately, DynamoDB is the default choice as it eliminates the need to provision and manage a Redis cluster. Tecton's feature platform allows developers to define Feature Views and assign them to an online store, automating the process of materializing features for real-time retrieval while providing transparent performance and cost monitoring. By combining multiple online stores and choosing between them at inference time, ML infrastructure teams can achieve significant performance increases and/or cost reductions depending on the variance in feature retrieval needs.

Company
Tecton

Date published
Oct. 10, 2023

Author(s)
Nick Acosta

Word count
1041

Language
English

Hacker News points
None found.


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