Category: HAQM SageMaker Canvas

We are excited to announce the launch of HAQM DocumentDB (with MongoDB compatibility) integration with HAQM SageMaker Canvas, allowing HAQM DocumentDB customers to build and use generative AI and machine learning (ML) solutions without writing code. HAQM DocumentDB is a fully managed native JSON document database that makes it straightforward and cost-effective to operate critical […]

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 […]

HAQM SageMaker Canvas is a no-code workspace that enables analysts and citizen data scientists to generate accurate machine learning (ML) predictions for their business needs. Starting today, SageMaker Canvas supports advanced model build configurations such as selecting a training method (ensemble or hyperparameter optimization) and algorithms, customizing the training and validation data split ratio, and […]

Data preparation is a crucial step in any machine learning (ML) workflow, yet it often involves tedious and time-consuming tasks. HAQM SageMaker Canvas now supports comprehensive data preparation capabilities powered by HAQM SageMaker Data Wrangler. With this integration, SageMaker Canvas provides customers with an end-to-end no-code workspace to prepare data, build and use ML and […]

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. This is the third post in a series discussing the integration of Salesforce Data Cloud and HAQM SageMaker. In Part 1 and Part 2, we show how the Salesforce Data Cloud and Einstein Studio integration with SageMaker allows businesses to access their […]

HAQM SageMaker Canvas is a rich, no-code Machine Learning (ML) and Generative AI workspace that has allowed customers all over the world to more easily adopt ML technologies to solve old and new challenges thanks to its visual, no-code interface. It does so by covering the ML workflow end-to-end: whether you’re looking for powerful data […]

Determining the value of housing is a classic example of using machine learning (ML). In this post, we discuss the use of an open-source model specifically designed for the task of visual question answering (VQA). With VQA, you can ask a question of a photo using natural language and receive an answer to your question—also in plain language. Our goal in this post is to inspire and demonstrate what is possible using this technology.

In the recent past, using machine learning (ML) to make predictions, especially for data in the form of text and images, required extensive ML knowledge for creating and tuning of deep learning models. Today, ML has become more accessible to any user who wants to use ML models to generate business value. With HAQM SageMaker […]

HAQM SageMaker Canvas now supports deploying machine learning (ML) models to real-time inferencing endpoints, allowing you take your ML models to production and drive action based on ML-powered insights. SageMaker Canvas is a no-code workspace that enables analysts and citizen data scientists to generate accurate ML predictions for their business needs. Until now, SageMaker Canvas […]

Enterprises seek to harness the potential of Machine Learning (ML) to solve complex problems and improve outcomes. Until recently, building and deploying ML models required deep levels of technical and coding skills, including tuning ML models and maintaining operational pipelines. Since its introduction in 2021, HAQM SageMaker Canvas has enabled business analysts to build, deploy, […]