AWS Machine Learning Blog For data scientists, moving machine learning (ML) models from proof of concept to production often presents a significant challenge. One of the main challenges can be deploying a well-performing, locally trained model to the cloud for inference and use in other applications. It can be cumbersome to manage the process, but […]Continue reading
AWS Machine Learning Blog Implementing a modern data architecture provides a scalable method to integrate data from disparate sources. By organizing data by business domains instead of infrastructure, each domain can choose tools that suit their needs. Organizations can maximize the value of their modern data architecture with generative AI solutions while innovating continuously. The […]Continue reading
AWS Machine Learning Blog This is a guest post by Dr. Naoki Okada, Lead Data Scientist at BrainPad Inc. Founded in 2004, BrainPad Inc. is a pioneering partner in the field of data utilization, helping companies create business and improve their management through the use of data. To date, BrainPad has helped more than 1,300 […]Continue reading
MIT News – Artificial intelligence Medical researchers are awash in a tsunami of clinical data. But we need major changes in how we gather, share, and apply this data to bring its benefits to all, says Leo Anthony Celi, principal research scientist at the MIT Laboratory for Computational Physiology (LCP). One key change is to […]Continue reading
Coronavirus | The Guardian Ten-year science strategy of UK Health Security Agency will use data to combat infectious diseases faster and more effectivelyHealth officials in the UK have drawn up plans for a “genomics transformation” that aims to detect and deal with outbreaks of infectious diseases faster and more effectively in the light of the […]Continue reading
MIT News – Artificial intelligence Words, data, and algorithms combine, An article about LLMs, so divine. A glimpse into a linguistic world, Where language machines are unfurled. It was a natural inclination to task a large language model (LLM) like CHATGPT with creating a poem that delves into the topic of large language models, and subsequently utilize […]Continue reading
AWS Machine Learning Blog Today, data scientists who are training deep learning models need to identify and remediate model training issues to meet accuracy targets for production deployment, and require a way to utilize standard tools for debugging model training. Among the data scientist community, TensorBoard is a popular toolkit that allows data scientists to […]Continue reading
AWS Machine Learning Blog Jupyter notebooks are highly favored by data scientists for their ability to interactively process data, build ML models, and test these models by making inferences on data. However, there are scenarios in which data scientists may prefer to transition from interactive development on notebooks to batch jobs. Examples of such use […]Continue reading
AWS Machine Learning Blog Globally, many organizations have critical business data dispersed among various content repositories, making it difficult to access this information in a streamlined and cohesive manner. Creating a unified and secure search experience is a significant challenge for organizations because each repository contains a wide range of document formats and access control […]Continue reading
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