phData

OCTOBER 27, 2023

After setup, your feature store knows where to pull features, but how do you go about updating your features? Creating the Feature Store This demo uses Feast as the feature store, Snowflake as the offline store, and Redis as the online store. Constructing Feature Engineering Pipelines Setting up a feature store is only a few steps.

Machine Learning 52

Machine Learning Machine Learning Python ML 52

Flipboard

OCTOBER 9, 2023

For example, containers exist to configure NGINX, Redis, and many other popular tools. Example: docker push suvadityamuk/docker-trial-pyimagesearch Example output: docker pull The inverse of the docker push command will allow you to pull any Docker image you may have access to.

Machine Learning 116

Machine Learning Machine Learning Deep Learning Deep Learning 116

IBM Journey to AI blog

OCTOBER 20, 2023

Data extraction: Platform capabilities help sort through complex details and quickly pull the necessary information from large documents. Summary generator: AI platforms can also transform dense text into a high-quality summary, capturing key points from financial reports, meeting transcriptions and more.

AI 72

AI AI Machine Learning Machine Learning 72

Snorkel AI

JUNE 27, 2023

And then you might want to load them into some key-value store like a database or Redis for faster retrieval. Which means that we require someone to pull data from multiple data sources. So from an additional perspective, batch prediction looks something like this. First, you generate predictions and you store them in a data warehouse.

AI 52

AI AI Data Warehouse Machine Learning 52

Snorkel AI

JUNE 27, 2023

And then you might want to load them into some key-value store like a database or Redis for faster retrieval. Which means that we require someone to pull data from multiple data sources. So from an additional perspective, batch prediction looks something like this. First, you generate predictions and you store them in a data warehouse.

AI 52

AI AI Data Warehouse Machine Learning 52

The MLOps Blog

OCTOBER 3, 2023

Aurimas: You can configure it against S3, DynamoDB, or Redis, for example. For example, if you wanted to use Cassandra or Redis as your, what we call the “inference store” or the “online store,” you can’t do that with a physical feature store. But yeah, for the most part. Feast, for example, is a library. Mikiko Bazeley: 100%.

ML 52

ML ML Data Scientist Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 30, 2023

Knowledge Bases supports databases with vector capabilities that store numerical representations of your data (embeddings) that models use to access this data for RAG, including Amazon OpenSearch Service, and other popular databases like Pinecone and Redis Enterprise Cloud (Amazon Aurora and MongoDB vector support coming soon).

AWS 121

AWS AI AI ML 121