Tag: ai/ml

Explainability of machine learning (ML) models used in the medical domain is becoming increasingly important because models need to be explained from a number of perspectives in order to gain adoption. These perspectives range from medical, technological, legal, and the most important perspective—the patient’s. Models developed on text in the medical domain have become accurate …

Read more “Explain medical decisions in clinical settings using Amazon SageMaker Clarify”

What if large language models (LLMs) had “vision”, the ability to understand the meaning of images? Just like we have seen the innovation with LLMs with chatbots and text data, the ability would make another huge impact on businesses by letting LLMs look at and organize millions of images in enterprise IT systems. In this …

Read more “What is Multimodal Search: “LLMs with vision” change businesses”

Posted by Ziniu Hu, Student Researcher, and Alireza Fathi, Research Scientist, Google Research, Perception Team There has been great progress towards adapting large language models (LLMs) to accommodate multimodal inputs for tasks including image captioning, visual question answering (VQA), and open vocabulary recognition. Despite such achievements, current state-of-the-art visual language models (VLMs) perform inadequately on …

Read more “Autonomous visual information seeking with large language models”

In our previous blog post, we presented Wayfair’s MLOps vision and how we implemented it within our Data Science & Machine Learning organization using Vertex AI, supported by tooling that we built in-house and state-of-the-art MLOps processes. We shared our MLOps reference architecture that includes a shared Python library that we built to interact with …

Read more “Implementing MLOps tools and processes for supply chain science at Wayfair”

Posted by Hussein Hazimeh, Research Scientist, Athena Team, and Riade Benbaki, Graduate Student at MIT Modern neural networks have achieved impressive performance across a variety of applications, such as language, mathematical reasoning, and vision. However, these networks often use large architectures that require lots of computational resources. This can make it impractical to serve such …

Read more “Neural network pruning with combinatorial optimization”

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker, a fully managed ML service, with requirements to develop features offline in a code …

Read more “Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift”