Category: Artificial Intelligence

AWS offers a broad set of artificial intelligence (AI) and machine learning (ML) services, including a suite of pre-trained, ready-to-use services for developers with no prior ML experience. In this post, we demonstrate how to use such services to build an application that fosters the inclusion of people with a visual or communication impairment, which […]

Today, customers can raise support tickets through multiple channels like – web, mobile, chat-bots, emails, or phone calls. When a support ticket is raised by a customer, it is processed and assigned to a category based on the information provided in the ticket. It is then routed to the support group for resolution according to […]

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). SageMaker JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions […]

This is a guest post by Andrew Degenholtz, CEO and Founder of eMagazines, the parent company of ReadAlong.ai. eMagazines’ technology seamlessly transforms print products into premium digital and audio experiences. Leveraging Amazon technology, ReadAlong.ai offers a simple, turn-key way for publishers to add audio to their websites with a single line of code. eMagazines supports […]

We are excited to announce that in Amazon Forecast, you can now start your forecast horizon at custom starting points, including on Sundays for weekly forecasts. This allows you to more closely align demand planning forecasts to local business practices and operational requirements. Forecast is a fully managed service that uses statistical and machine learning […]

We’re excited to announce that you can now automatically monitor the accuracy of your Amazon Forecast predictors over time. As new data is provided, Forecast automatically computes predictor accuracy metrics on the new dataset, providing you with more information to decide whether to keep using, retrain, or create new predictors. Monitoring predictor quality and identifying […]

September 2023: This post was reviewed and updated for accuracy. Data fuels machine learning (ML); the quality of data has a direct impact on the quality of ML models. Therefore, improving data quality and employing the right feature engineering techniques are critical to creating accurate ML models. ML practitioners often tediously iterate on feature engineering, […]

In today’s digital landscape, customers are expecting a high-quality experience that is responsive and delightful. Chatbots and virtual assistants have transformed the customer experience from a point-and-click or a drag-and-drop experience to one that is driven by voice or text. You can create a more engaging experience by further augmenting the interaction with a visual […]

Data scientists and machine learning (ML) engineers often prepare their data before building ML models. Data preparation typically includes data preprocessing and feature engineering. You preprocess data by transforming data into the right shape and quality for training, and you engineer features by selecting, transforming, and creating variables when building a predictive model. Amazon SageMaker […]

In this post, we talk about how to split a machine learning (ML) dataset into train, test, and validation datasets with Amazon SageMaker Data Wrangler so you can easily split your datasets with minimal to no code. Data used for ML is typically split into the following datasets: Training – Used to train an algorithm […]