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Run ML inference on unplanned and spiky traffic using Amazon SageMaker multi-model endpoints | Amazon Web Services

Amazon SageMaker multi-model endpoints (MMEs) are a fully managed capability of SageMaker inference that allows you to deploy thousands of models on a single...

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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...

How Meesho built a generalized feed ranker using Amazon SageMaker inference | Amazon Web Services

This is a guest post co-written by Rama Badrinath, Divay Jindal and Utkarsh Agrawal at Meesho. Meesho is India’s fastest growing ecommerce company...

Build and deploy ML inference applications from scratch using Amazon SageMaker | Amazon Web Services

As machine learning (ML) goes mainstream and gains wider adoption, ML-powered inference applications are becoming increasingly common to solve a range of complex business...

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