Favorite 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

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Shared by AWS Machine Learning June 28, 2023

Favorite 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

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Shared by AWS Machine Learning June 28, 2023

Favorite 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

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Shared by AWS Machine Learning June 27, 2023

Favorite 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

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Shared by AWS Machine Learning June 24, 2023

Favorite 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

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Shared by AWS Machine Learning June 24, 2023

Favorite 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

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Shared by AWS Machine Learning June 23, 2023

Favorite 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

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Shared by AWS Machine Learning June 22, 2023

Favorite 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

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Shared by AWS Machine Learning June 22, 2023

Favorite Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. Increasingly, sustainability (energy efficiency) is becoming an additional objective for customers. This is important because training ML models and then using the trained models to make predictions (inference) can be

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Shared by AWS Machine Learning June 21, 2023

Favorite 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 domain—a construct that encapsulates your ML

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Shared by AWS Machine Learning June 20, 2023