AWS Machine Learning Blog

Category: Artificial Intelligence

Use proprietary foundation models from Amazon SageMaker JumpStart in Amazon SageMaker Studio

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can discover and deploy publicly available and proprietary foundation models to dedicated Amazon SageMaker instances for your generative AI applications. SageMaker JumpStart allows you to deploy foundation models from a network isolated environment, and […]

How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

This blog post is co-written with Marat Adayev and Dmitrii Evstiukhin from Provectus. When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. Machine Learning Operations (MLOps) provides the technical solution to this issue, assisting organizations in managing, […]

Define customized permissions in minutes with Amazon SageMaker Role Manager via the AWS CDK

Machine learning (ML) administrators play a critical role in maintaining the security and integrity of ML workloads. Their primary focus is to ensure that users operate with the utmost security, adhering to the principle of least privilege. However, accommodating the diverse needs of different user personas and creating appropriate permission policies can sometimes impede agility. […]

Accelerate time to business insights with the Amazon SageMaker Data Wrangler direct connection to Snowflake

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code. SageMaker Data Wrangler supports Snowflake, a popular […]

Deploy a serverless ML inference endpoint of large language models using FastAPI, AWS Lambda, and AWS CDK

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 with the right tool, […]

customized neural network model architecture

How Light & Wonder built a predictive maintenance solution for gaming machines on AWS

This post is co-written with Aruna Abeyakoon and Denisse Colin from Light and Wonder (L&W). Headquartered in Las Vegas, Light & Wonder, Inc. is the leading cross-platform global game company that provides gambling products and services. Working with AWS, Light & Wonder recently developed an industry-first secure solution, Light & Wonder Connect (LnW Connect), to […]

Use the AWS CDK to deploy Amazon SageMaker Studio lifecycle configurations

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). Studio provides a single web-based visual interface where you can perform all ML development steps required to prepare data, as well as build, train, and deploy models. Lifecycle configurations are shell scripts triggered by Studio lifecycle events, such as starting […]

Start of call and Post call (summary) Lambda Hooks that integrate with Salesforce to look-up and update Case records respectively

Boost agent productivity with Salesforce integration for Live Call Analytics

As a contact center agent, would you rather focus on having productive customer conversations or get distracted by having to look up customer information and knowledge articles that could exist in various systems? We’ve all been there. Having a productive conversation while multitasking is challenging. A single negative experience may put a dent on a […]

Onboard users to Amazon SageMaker Studio with Active Directory group-specific IAM roles

November 2023: This post was updated to include the Amazon SageMaker APIs. Amazon SageMaker Studio is a web-based integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. For provisioning Studio in your AWS account and Region, you first need to create an Amazon SageMaker […]

SambaSafety automates custom R workload, improving driver safety with Amazon SageMaker and AWS Step Functions

At SambaSafety, their mission is to promote safer communities by reducing risk through data insights. Since 1998, SambaSafety has been the leading North American provider of cloud–based mobility risk management software for organizations with commercial and non–commercial drivers. SambaSafety serves more than 15,000 global employers and insurance carriers with driver risk and compliance monitoring, online […]