Category: Intermediate (200)

Today, we are excited to announce support for Code Editor, a new integrated development environment (IDE) option in Amazon SageMaker Studio. Code Editor is based on Code-OSS, Visual Studio Code Open Source, and provides access to the familiar environment and tools of the popular IDE that machine learning (ML) developers know and love, fully integrated […]

Amazon SageMaker makes it straightforward to deploy machine learning (ML) models for real-time inference and offers a broad selection of ML instances spanning CPUs and accelerators such as AWS Inferentia. As a fully managed service, you can scale your model deployments, minimize inference costs, and manage your models more effectively in production with reduced operational […]

Amazon SageMaker Canvas is a no-code workspace that enables analysts and citizen data scientists to generate accurate machine learning (ML) predictions for their business needs. Starting today, SageMaker Canvas supports advanced model build configurations such as selecting a training method (ensemble or hyperparameter optimization) and algorithms, customizing the training and validation data split ratio, and […]

Digital publishers are continuously looking for ways to streamline and automate their media workflows to generate and publish new content as rapidly as they can, but without foregoing quality. Adding images to capture the essence of text can improve the reading experience. Machine learning techniques can help you discover such images. “A striking image is […]

The risks associated with generative AI have been well-publicized. Toxicity, bias, escaped PII, and hallucinations negatively impact an organization’s reputation and damage customer trust. Research shows that not only do risks for bias and toxicity transfer from pre-trained foundation models (FM) to task-specific generative AI services, but that tuning an FM for specific tasks, on […]

Data preparation is a crucial step in any machine learning (ML) workflow, yet it often involves tedious and time-consuming tasks. Amazon SageMaker Canvas now supports comprehensive data preparation capabilities powered by Amazon SageMaker Data Wrangler. With this integration, SageMaker Canvas provides customers with an end-to-end no-code workspace to prepare data, build and use ML and […]

In today’s ever-evolving world of ecommerce, the influence of a compelling product description cannot be overstated. It can be the decisive factor that turns a potential visitor into a paying customer or sends them clicking off to a competitor’s site. The manual creation of these descriptions across a vast array of products is a labor-intensive […]

This post was co-written with Greg Benson, Chief Scientist; Aaron Kesler, Sr. Product Manager; and Rich Dill, Enterprise Solutions Architect from SnapLogic. Many customers are building generative AI apps on Amazon Bedrock and Amazon CodeWhisperer to create code artifacts based on natural language. This use case highlights how large language models (LLMs) are able to […]

The financial service (FinServ) industry has unique generative AI requirements related to domain-specific data, data security, regulatory controls, and industry compliance standards. In addition, customers are looking for choices to select the most performant and cost-effective machine learning (ML) model and the ability to perform necessary customization (fine-tuning) to fit their business use cases. Amazon […]

For decades, Amazon has pioneered and innovated machine learning (ML), bringing delightful experiences to its customers. From the earliest days, Amazon has used ML for various use cases such as book recommendations, search, and fraud detection. Similar to the rest of the industry, the advancements of accelerated hardware have allowed Amazon teams to pursue model […]