Use proprietary foundation models from Amazon SageMaker JumpStart in Amazon SageMaker Studio
Using Amazon SageMaker JumpStart to deploy proprietary foundation models in Amazon SageMaker Studio for generative AI applications
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps
Understanding optimal transport theory and proposing a new model for transport maps in high-dimensional spaces using feature-sparse maps and translation invariant costs.
Live from the Lakehouse
Covering the highlights and key takeaways from this year's Data and AI Summit with 240+ technical sessions, keynotes, and meetups.
Day of AI curriculum meets the moment
Global participation in MIT RAISE’s free K-12 program doubles in its second year.
Declarative Data Pipelines with Hoptimator
This blogpost discusses how Hoptimator enables declarative data pipelines for self-service infrastructure at LinkedIn.
How to form yes or no questions in English
A guide on forming yes or no questions in English
What are the rules for "wh" questions in English?
Exploring the formation of 'wh' questions in English, including 'who', 'what', 'where', 'when', 'why', and 'how'.
What are tag questions and how do you use them?
Exploring the usage and significance of English tag questions.
All about English questions
Exploring the mechanics and formation of English questions in a comprehensive guide.
Define customized permissions in minutes with Amazon SageMaker Role Manager via the AWS CDK
Defining customized permissions quickly and easily with Amazon SageMaker Role Manager using the AWS CDK for machine learning administrators to ensure user security
LLMs high priority for enterprise data science, but concerns remain
LLMs high priority for enterprise data science, but concerns remain
Preference learning with automated feedback for cache eviction
Preference learning with automated feedback for cache eviction is a technical blogpost that introduces the HALP framework, a scalable cache eviction framework based on learned rewards and preference learning with automated feedback. The framework improves infrastructure efficiency and user video playback latency for YouTube's content delivery network.