Tag: Container registry
Critical Bugs Put Hugging Face AI Platform in a ‘Pickle’
Two critical security vulnerabilities in the Hugging Face AI platform opened the door to attackers looking to access and alter customer data and models.One...
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Modernizing data science lifecycle management with AWS and Wipro | Amazon Web Services
This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination...
Foundational data protection for enterprise LLM acceleration with Protopia AI | Amazon Web Services
This post is written in collaboration with Balaji Chandrasekaran, Jennifer Cwagenberg and Andrew Sansom and Eiman Ebrahimi from Protopia AI. New and powerful large...
Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 2: Interactive User Experiences in SageMaker Studio | Amazon Web Services
Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML)...
Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 1: PySDK Improvements | Amazon Web Services
Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML)...
New – Code Editor, based on Code-OSS VS Code Open Source now available in Amazon SageMaker Studio | Amazon Web Services
Today, we are excited to announce support for Code Editor, a new integrated development environment (IDE) option in Amazon SageMaker Studio. Code Editor is...
Machine Learning with MATLAB and Amazon SageMaker | Amazon Web Services
This post is written in collaboration with Brad Duncan, Rachel Johnson and Richard Alcock from MathWorks. MATLAB  is a popular programming tool for a...
Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker | Amazon Web Services
This is a joint blog with AWS and Philips. Philips is a health technology company focused on improving people’s lives through meaningful innovation. Since...
Explore advanced techniques for hyperparameter optimization with Amazon SageMaker Automatic Model Tuning | Amazon Web Services
Creating high-performance machine learning (ML) solutions relies on exploring and optimizing training parameters, also known as hyperparameters. Hyperparameters are the knobs and levers that...
Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD | Amazon Web Services
Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is...
Build a medical imaging AI inference pipeline with MONAI Deploy on AWS | Amazon Web Services
This post is cowritten with Ming (Melvin) Qin, David Bericat and Brad Genereaux from NVIDIA. Medical imaging AI researchers and developers need a scalable,...
Dialogue-guided visual language processing with Amazon SageMaker JumpStart | Amazon Web Services
Visual language processing (VLP) is at the forefront of generative AI, driving advancements in multimodal learning that encompasses language intelligence, vision understanding, and processing....
Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker | Amazon Web Services
Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative...
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