article thumbnail

Improving air quality with generative AI

AWS Machine Learning Blog

This manual synchronization process, hindered by disparate data formats, is resource-intensive, limiting the potential for widespread data orchestration. The platform, although functional, deals with CSV and JSON files containing hundreds of thousands of rows from various manufacturers, demanding substantial effort for data ingestion.

article thumbnail

Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor

AWS Machine Learning Blog

For this use case, we see how SageMaker Feature Store helps convert the raw car sales data into structured features. You should see two pipelines created: car-data-ingestion-pipeline and car-data-aggregated-ingestion-pipeline. Choose the car-data-ingestion-pipeline. Choose the car-data feature group.

ML 117
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning Blog

MongoDB Atlas offers automatic sharding, horizontal scalability, and flexible indexing for high-volume data ingestion. Among all, the native time series capabilities is a standout feature, making it ideal for a managing high volume of time-series data, such as business critical application data, telemetry, server logs and more.

article thumbnail

Cassandra vs MongoDB

Pickl AI

It was initially developed at Facebook to address the challenges of managing massive data volumes for their inbox search feature. Released as an open-source project in 2008 and later becoming a top-level project of the Apache Software Foundation in 2010, Cassandra has gained popularity due to its scalability and high availability features.

article thumbnail

A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

This makes GPUs well suited for data-heavy, matrix math-based, ML training workloads, and real-time inference workloads needing synchronicity at scale. Both use cases require the ability to move data around the chip quickly and controllably.

ML 98
article thumbnail

Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

Data flow Here is an example of this data flow for an Agent Creator pipeline that involves data ingestion, preprocessing, and vectorization using Chunker and Embedding Snaps. He currently is working on Generative AI for data integration.