AWS FeedUnderstanding the key capabilities of Amazon SageMaker Feature Store One of the challenging parts of machine learning (ML) is feature engineering, the process of transforming data to create features for ML. Features are processed data signals used for training ML models and for deployed models to make accurate predictions. Data scientists and ML engineers…
AWS FeedOrchestrating analytics jobs on Amazon EMR Notebooks using Amazon MWAA In a previous post, we introduced the Amazon EMR notebook APIs, which allow you to programmatically run a notebook on both Amazon EMR Notebooks and Amazon EMR Studio (preview) without accessing the AWS web console. With the APIs, you can schedule running EMR notebooks…
AWS FeedNew – Multiple Private Marketplace Catalogs We launched AWS Marketplace in 2014, and it allows customers to find, buy, and immediately start using cloud-based applications developed by independent software vendors (ISVs). In 2018, we added the ability to add a Private Marketplace where you can curate a list of approved products your users can…
AWS FeedAmazon Elasticsearch Service extends encryption at rest and node-to-node encryption to existing domains Amazon Elasticsearch Service now supports encryption of data at rest and node-to-node encryption on existing domains, enabling organizations hosting sensitive workloads to meet stringent security and compliance requirements.
This release includes new memory and current control functions as well as new features in the SDK.
Data Labeling in Azure Machine learning gives you a central place to create, manage, and monitor labeling projects.