AWS Machine Learning Blog

Structured data, defined as data following a fixed pattern such as information stored in columns within databases, and unstructured data, which lacks a specific form or pattern like text, images, or social media posts, both continue to grow as they are produced and consumed by various organizations. For instance, according to International Data Corporation (IDC), […]

The post describes how you can overcome the challenges of retaining data ownership and preserving data privacy while using LLMs by deploying Protopia AI’s Stained Glass Transform to protect your data. Protopia AI has partnered with AWS to deliver the critical component of data protection and ownership for secure and efficient enterprise adoption of generative AI. This post outlines the solution and demonstrates how it can be used in AWS for popular enterprise use cases like Retrieval Augmented Generation (RAG) and with state-of-the-art LLMs like Llama 2.

This is a guest post co-authored by Nafi Ahmet Turgut, Hasan Burak Yel, and Damla Şentürk from Getir. Established in 2015, Getir has positioned itself as the trailblazer in the sphere of ultrafast grocery delivery. This innovative tech company has revolutionized the last-mile delivery segment with its compelling offering of “groceries in minutes.” With a […]

The ability to quickly build and deploy machine learning (ML) models is becoming increasingly important in today’s data-driven world. However, building ML models requires significant time, effort, and specialized expertise. From data collection and cleaning to feature engineering, model building, tuning, and deployment, ML projects often take months for developers to complete. And experienced data […]

Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. As we continue to innovate to increase data science productivity, we’re excited to announce the improved SageMaker Studio experience, which allows users to select the managed Integrated Development Environment (IDE) […]

As organizations scale the adoption of machine learning (ML), they are looking for efficient and reliable ways to deploy new infrastructure and onboard teams to ML environments. One of the challenges is setting up authentication and fine-grained permissions for users based on their roles and activities. For example, MLOps engineers typically perform model deployment activities, […]

We believe generative AI has the potential over time to transform virtually every customer experience we know. The number of companies launching generative AI applications on AWS is substantial and building quickly, including adidas, Booking.com, Bridgewater Associates, Clariant, Cox Automotive, GoDaddy, and LexisNexis Legal & Professional, to name just a few. Innovative startups like Perplexity […]

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. SageMaker makes it easy to deploy models into production directly through API calls to the service. Models are packaged into containers for robust and scalable deployments. SageMaker provides […]

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. SageMaker makes it straightforward to deploy models into production directly through API calls to the service. Models are packaged into containers for robust and scalable deployments. Although […]

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 […]