In the last few years Large Language Models (LLMs) have risen to prominence as outstanding tools capable of understanding, generating and manipulating text with unprecedented proficiency. Their potential applications span from conversational agents to content generation and information retrieval, holding …
Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a …
This post is co-written with Marc Neumann, Amor Steinberg and Marinus Krommenhoek from BMW Group.
The BMW Group – headquartered in Munich, Germany – is driven by 149,000 employees worldwide and manufactures in over 30 production and assembly facilities across …
Retrieval Augmented Generation (RAG) allows you to provide a large language model (LLM) with access to data from external knowledge sources such as repositories, databases, and APIs without the need to fine-tune it. When using generative AI for question answering, …
Creating high-performance machine learning (ML) solutions relies on exploring and optimizing training parameters, also known as hyperparameters. Hyperparameters are the knobs and levers that we use to adjust the training process, such as learning rate, batch size, regularization strength, and …
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, …
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 …
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 …
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 …
Radiology reports are comprehensive, lengthy documents that describe and interpret the results of a radiological imaging examination. In a typical workflow, the radiologist supervises, reads, and interprets the images, and then concisely summarizes the key findings. The summarization (or impression…
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 …
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 …
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 …
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 …
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 …
Drug development is a complex and long process that involves screening thousands of drug candidates and using computational or experimental methods to evaluate leads. According to McKinsey, a single drug can take 10 years and cost an average of $2.6 …
Today we are excited to announce that Stable Diffusion XL 1.0 (SDXL 1.0) is available for customers through Amazon SageMaker JumpStart. SDXL 1.0 is the latest image generation model from Stability AI. SDXL 1.0 enhancements include native 1024-pixel image generation …
With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is a core part of every business and industry.
Amazon SageMaker Studio is the first fully integrated …
As more and more customers are looking to put machine learning (ML) workloads in production, there is a large push in organizations to shorten the development lifecycle of ML code. Many organizations prefer writing their ML code in a production-ready …
This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. A public GitHub repo provides hands-on examples for each of the presented approaches.
Amazon SageMaker Studio is …