AWS Machine Learning Blog

You may have tables in your Amazon Redshift data warehouse or in your Amazon Simple Storage Service (Amazon S3) data lake full of records containing customer case notes, product reviews, and social media messages, in many languages. Your task is to identify the products that people are talking about, determine if they’re expressing happy thoughts […]

More customers are finding the need to build larger, scalable, and more cost-effective machine learning (ML) inference pipelines in the cloud. Outside of these base prerequisites, the requirements of ML inference pipelines in production vary based on the business use case. A typical inference architecture for applications like recommendation engines, sentiment analysis, and ad ranking […]

Amazon SageMaker projects are AWS Service Catalog provisioned products that enable you to easily create end-to-end machine learning (ML) solutions. SageMaker projects give organizations the ability to use templates that bootstrap ML solutions for your users to speed up the start time for ML development. You can now use SageMaker projects to manage custom dependencies […]

Starting today, we’re releasing new tools for multimodal financial analysis within Amazon SageMaker JumpStart. SageMaker JumpStart helps you quickly and easily get started with machine learning (ML). It provides a set of solutions for the most common use cases that can be trained and deployed readily with just a few clicks. You can now access […]

Starting today, we’re releasing new tools for multimodal financial analysis within Amazon SageMaker JumpStart. SageMaker JumpStart helps you quickly and easily get started with machine learning (ML) and provides a set of solutions for the most common use cases that can be trained and deployed readily with just a few clicks. You can now access […]

You can deploy machine learning (ML) models for real-time inference with large libraries or pre-trained models. Common use cases include sentiment analysis, image classification, and search applications. These ML jobs typically vary in duration and require instant scaling to meet peak demand. You want to process latency-sensitive inference requests and pay only for what you […]

Proactively detecting unusual or unexpected variances in your business metrics and reducing false alarms can help you stay on top of sudden changes and improve your business performance. Accurately identifying the root cause of deviation from normal business metrics and taking immediate steps to remediate an anomaly can not only boost user engagement but also […]

Based in Dubai, Ounass is the Middle East’s leading ecommerce platform for luxury goods. Scouring the globe for leading trends, Ounass’s expert team reports on the latest fashion updates, coveted insider information, and exclusive interviews for customers to read and shop. With more than 230,000 unique catalog items spanning multiple brands and several product classes—including […]

Conversational interfaces have the potential to allow customers to interact more naturally with automated systems. From virtual assistants to concierge chatbots, conversational interfaces can bring convenience and personalization to customers at scale. However, these benefits depend not only on the technology that the Amazon Lex platform and other AWS services can provide, but also on […]

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. It provides all the tools you need to take your models from experimentation to production while boosting your productivity. You can write code, track experiments, visualize data, and perform […]