Category: Amazon SageMaker Studio

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries. However, implementing security, data privacy, and governance controls are still key challenges faced by customers when implementing ML […]

This post is written in collaboration with Dima Zadorozhny and Fuad Babaev from VirtuSwap. VirtuSwap is a startup company developing innovative technology for decentralized exchange of assets on blockchains. VirtuSwap’s technology provides more efficient trading for assets that don’t have a direct pair between them. The absence of a direct pair leads to costly indirect trading, […]

Today, we are excited to announce the simplified Quick setup experience in Amazon SageMaker. With this new capability, individual users can launch Amazon SageMaker Studio with default presets in minutes. SageMaker Studio is an integrated development environment (IDE) for machine learning (ML). ML practitioners can perform all ML development steps—from preparing their data to building, […]

Amazon SageMaker Domain supports SageMaker machine learning (ML) environments, including SageMaker Studio and SageMaker Canvas. SageMaker Studio is a fully integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all ML development steps, from preparing data to building, training, and deploying your ML models, improving […]

This post demonstrates a strategy for fine-tuning publicly available LLMs for the task of radiology report summarization using AWS services. LLMs have demonstrated remarkable capabilities in natural language understanding and generation, serving as foundation models that can be adapted to various domains and tasks. There are significant benefits to using a pre-trained model. It reduces computation costs, reduces carbon footprints, and allows you to use state-of-the-art models without having to train one from scratch.

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 […]

Amazon SageMaker offers several ways to run distributed data processing jobs with Apache Spark, a popular distributed computing framework for big data processing. You can run Spark applications interactively from Amazon SageMaker Studio by connecting SageMaker Studio notebooks and AWS Glue Interactive Sessions to run Spark jobs with a serverless cluster. With interactive sessions, you […]

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. We’re excited to announce Amazon SageMaker and Salesforce Data Cloud integration. With this capability, businesses can access their Salesforce data securely with a zero-copy approach using SageMaker and use SageMaker tools to build, train, and deploy AI models. The inference endpoints are […]

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, […]

SageMaker Distribution is a pre-built Docker image containing many popular packages for machine learning (ML), data science, and data visualization. This includes deep learning frameworks like PyTorch, TensorFlow, and Keras; popular Python packages like NumPy, scikit-learn, and pandas; and IDEs like JupyterLab. In addition to this, SageMaker Distribution supports conda, micromamba, and pip as Python […]