AWS Machine Learning Blog

FEBRUARY 1, 2024

Data is the foundation to capturing the maximum value from AI technology and solving business problems quickly. To unlock the potential of generative AI technologies, however, there’s a key prerequisite: your data needs to be appropriately prepared. Prerequisites Before starting, you need an AWS account.

Data Preparation 85

Data Preparation AWS AI AI 85

AWS Machine Learning Blog

OCTOBER 18, 2023

Purina used artificial intelligence (AI) and machine learning (ML) to automate animal breed detection at scale. The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring.

AWS 93

AWS ML ML Machine Learning 93

AWS Machine Learning Blog

MARCH 8, 2023

In order to train a model using data stored outside of the three supported storage services, the data first needs to be ingested into one of these services (typically Amazon S3). This requires building a data pipeline (using tools such as Amazon SageMaker Data Wrangler ) to move data into Amazon S3.

ML 104

ML ML AWS Python 104

Flipboard

NOVEMBER 24, 2023

To pave the way for the growth of AI, BMW Group needed to make a leap regarding scalability and elasticity while reducing operational overhead, software licensing, and hardware management. These platforms were too limited regarding CPU, GPU, and memory to allow the scalability of AI at BMW Group.

ML 95

ML ML AWS AI 95

AWS Machine Learning Blog

MARCH 1, 2023

In this post, we share how Kakao Games and the Amazon Machine Learning Solutions Lab teamed up to build a scalable and reliable LTV prediction solution by using AWS data and ML services such as AWS Glue and Amazon SageMaker. The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account.

AWS 88

AWS ML ML ETL 88

AWS Machine Learning Blog

SEPTEMBER 18, 2023

Ray AI Runtime (AIR) reduces friction of going from development to production. Amazon SageMaker Pipelines allows orchestrating the end-to-end ML lifecycle from data preparation and training to model deployment as automated workflows. We set up an end-to-end Ray-based ML workflow, orchestrated using SageMaker Pipelines.

Machine Learning 99

Machine Learning Machine Learning ML ML 99

Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. To do this, we provide an AWS CloudFormation template to create a stack that contains the resources.

ML 93

ML ML AWS Data Warehouse 93