Artificial Intelligence
Author: James Wu
James Wu is a Senior AI/ML Specialist Solution Architect at AWS. helping customers design and build AI/ML solutions. James’s work covers a wide range of ML use cases, with a primary interest in computer vision, deep learning, and scaling ML across the enterprise. Prior to joining AWS, James was an architect, developer, and technology leader for over 10 years, including 6 years in engineering and 4 years in marketing & advertising industries.
Accelerate development of ML workflows with Amazon Q Developer in Amazon SageMaker Studio
In this post, we present a real-world use case analyzing the Diabetes 130-US hospitals dataset to develop an ML model that predicts the likelihood of readmission after discharge.
Achieve up to ~2x higher throughput while reducing costs by up to ~50% for generative AI inference on Amazon SageMaker with the new inference optimization toolkit – Part 2
As generative artificial intelligence (AI) inference becomes increasingly critical for businesses, customers are seeking ways to scale their generative AI operations or integrate generative AI models into existing workflows. Model optimization has emerged as a crucial step, allowing organizations to balance cost-effectiveness and responsiveness, improving productivity. However, price-performance requirements vary widely across use cases. For […]
Model management for LoRA fine-tuned models using Llama2 and Amazon SageMaker
In the era of big data and AI, companies are continually seeking ways to use these technologies to gain a competitive edge. One of the hottest areas in AI right now is generative AI, and for good reason. Generative AI offers powerful solutions that push the boundaries of what’s possible in terms of creativity and […]
Build a personalized avatar with generative AI using Amazon SageMaker
Generative AI has become a common tool for enhancing and accelerating the creative process across various industries, including entertainment, advertising, and graphic design. It enables more personalized experiences for audiences and improves the overall quality of the final products. One significant benefit of generative AI is creating unique and personalized experiences for users. For example, […]
Achieve high performance at scale for model serving using Amazon SageMaker multi-model endpoints with GPU
Amazon SageMaker multi-model endpoints (MMEs) provide a scalable and cost-effective way to deploy a large number of machine learning (ML) models. It gives you the ability to deploy multiple ML models in a single serving container behind a single endpoint. From there, SageMaker manages loading and unloading the models and scaling resources on your behalf […]