Remove Accountability Remove APIs Remove Big data Remove Engineering
article thumbnail

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

article thumbnail

Use Amazon SageMaker pipeline sharing to view or manage pipelines across AWS accounts

AWS Machine Learning

On August 9, 2022, we announced the general availability of cross-account sharing of Amazon SageMaker Pipelines entities. You can now use cross-account support for Amazon SageMaker Pipelines to share pipeline entities across AWS accounts and access shared pipelines directly through Amazon SageMaker API calls.

Insiders

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

Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

It allows developers to build and scale generative AI applications using FMs through an API, without managing infrastructure. Customers are building innovative generative AI applications using Amazon Bedrock APIs using their own proprietary data.

APIs 127
article thumbnail

How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning

This includes gathering, exploring, and understanding the business and technical aspects of the data, along with evaluation of any manipulations that may be needed for the model building process. One aspect of this data preparation is feature engineering. However, generalizing feature engineering is challenging.

article thumbnail

Senior back-end software engineer

Stratifyd

We are seeking an analytical, results-driven individual with a passion for developing applications and improving existing ones who knows how to get things done and is not afraid to take on big challenges. As a Senior Back-End Software Engineer with Stratifyd, Inc. Key accountabilities & responsibilities. and Python/C API.

article thumbnail

­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

As feature data grows in size and complexity, data scientists need to be able to efficiently query these feature stores to extract datasets for experimentation, model training, and batch scoring. The offline store data is stored in an Amazon Simple Storage Service (Amazon S3) bucket in your AWS account.

Scripts 75
article thumbnail

Simplify data prep for generative AI with Amazon SageMaker Data Wrangler

AWS Machine Learning

However, these models require massive amounts of clean, structured training data to reach their full potential. Most real-world data exists in unstructured formats like PDFs, which requires preprocessing before it can be used effectively. According to IDC , unstructured data accounts for over 80% of all business data today.