Introduction In today’s application development landscape, organizations are widely adopting Infrastructure-as-Code (IaC) technology to automate the provisioning and management of resources to support cloud native applications and workloads across their multi-cloud environments. By utilizing IaC, o...
Introduction In today’s application development landscape, organizations are widely adopting Infrastructure-as-Code (IaC) technology to automate the provisioning and management of resources to support cloud native applications and workloads across their multi-cloud environments. By utilizing IaC, o...
Introduction The Azure Firewall is a cloud-native and intelligent network firewall security service that can be integrated into many different use cases. It’s a fully stateful firewall as a service with built-in high availability and unrestricted cloud scalability that provides both east-west and no...
Today we are excited to announce that Together Computer’s GPT-NeoXT-Chat-Base-20B language foundation model is available for customers using Amazon SageMaker JumpStart. GPT-NeoXT-Chat-Base-20B is an open-source model to build conversational bots. You can easily try out this model and use it with JumpStart. JumpStart is the machine learning (ML) hub of Amazon SageMaker that provides access […]
AWS delivers services that meet customers’ artificial intelligence (AI) and machine learning (ML) needs with services ranging from custom hardware like AWS Trainium and AWS Inferentia to generative AI foundation models (FMs) on Amazon Bedrock. In February 2022, AWS and Hugging Face announced a collaboration to make generative AI more accessible and cost efficient. Generative […]
🩺 Is the medical field ready for AI? Join the discussion with MIT's latest insights on the role and impact of AI in healthcare: https://news.mit.edu/2023/is-medicine-ready-for-ai-0517
💡 #Google is paving the way for more privacy in Machine Learning models with DP-SGD.
Find out more about this significant enhancement: http://ai.googleblog.com/2023/05/making-ml-models-differentially-private.html