AWS Machine Learning Blog

Category: Amazon SageMaker Canvas


Amazon SageMaker Domain in VPC only mode to support SageMaker Studio with auto shutdown Lifecycle Configuration and SageMaker Canvas with Terraform

Amazon SageMaker Domain supports SageMaker machine learning (ML) environments, including SageMaker Studio and SageMaker Canvas. SageMaker Studio is a fully integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all ML development steps, from preparing data to building, training, and deploying your ML models, improving […]

Accelerate business outcomes with 70% performance improvements to data processing, training, and inference with Amazon SageMaker Canvas

Amazon SageMaker Canvas is a visual interface that enables business analysts to generate accurate machine learning (ML) predictions on their own, without requiring any ML experience or having to write a single line of code. SageMaker Canvas’s intuitive user interface lets business analysts browse and access disparate data sources in the cloud or on premises, […]

Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

If you are a business analyst, understanding customer behavior is probably one of the most important things you care about. Understanding the reasons and mechanisms behind customer purchase decisions can facilitate revenue growth. However, the loss of customers (commonly referred to as customer churn) always poses a risk. Gaining insights into why customers leave can […]

Democratize computer vision defect detection for manufacturing quality using no-code machine learning with Amazon SageMaker Canvas

Cost of poor quality is top of mind for manufacturers. Quality defects increase scrap and rework costs, decrease throughput, and can impact customers and company reputation. Quality inspection on the production line is crucial for maintaining quality standards. In many cases, human visual inspection is used to assess the quality and detect defects, which can […]

Capture public health insights more quickly with no-code machine learning using Amazon SageMaker Canvas

Public health organizations have a wealth of data about different types of diseases, health trends, and risk factors. Their staff has long used statistical models and regression analyses to make important decisions such as targeting populations with the highest risk factors for a disease with therapeutics, or forecasting the progression of concerning outbreaks. When public […]

Retrain ML models and automate batch predictions in Amazon SageMaker Canvas using updated datasets

You can now retrain machine learning (ML) models and automate batch prediction workflows with updated datasets in Amazon SageMaker Canvas, thereby making it easier to constantly learn and improve the model performance and drive efficiency. An ML model’s effectiveness depends on the quality and relevance of the data it’s trained on. As time progresses, the […]

Use Amazon SageMaker Canvas to build machine learning models using Parquet data from Amazon Athena and AWS Lake Formation

Data is the foundation for machine learning (ML) algorithms. One of the most common formats for storing large amounts of data is Apache Parquet due to its compact and highly efficient format. This means that business analysts who want to extract insights from the large volumes of data in their data warehouse must frequently use […]

Prepare training and validation dataset for facies classification using Snowflake integration and train using Amazon SageMaker Canvas

This post is co-written with Thatcher Thornberry from bpx energy.  Facies classification is the process of segmenting lithologic formations from geologic data at the wellbore location. During drilling, wireline logs are obtained, which have depth-dependent geologic information. Geologists are deployed to analyze this log data and determine depth ranges for potential facies of interest from […]

Operationalize ML models built in Amazon SageMaker Canvas to production using the Amazon SageMaker Model Registry

You can now register machine learning (ML) models built in Amazon SageMaker Canvas with a single click to the Amazon SageMaker Model Registry, enabling you to operationalize ML models in production. Canvas is a visual interface that enables business analysts to generate accurate ML predictions on their own—without requiring any ML experience or having to […]

Publish predictive dashboards in Amazon QuickSight using ML predictions from Amazon SageMaker Canvas

Understanding business trends, customer behavior, sales revenue, increase in demand, and buyer propensity all start with data. Exploring, analyzing, interpreting, and finding trends in data is essential for businesses to achieve successful outcomes. Business analysts play a pivotal role in facilitating data-driven business decisions through activities such as the visualization of business metrics and the […]