No-Code Machine Learning Using Amazon AWS SageMaker Canvas

Explore Amazon Web Services, the world's most comprehensive cloud platform with over 200 services from data centers across the globe, enabling organizations to manage servers, storage, networking, and security.

Discover how Amazon SageMaker Canvas enables no-code machine learning, with SageMaker Studio and Canvas interfaces, data wrangling, model training, deployment to production, and monitoring.

Configure a SageMaker domain and users to launch SageMaker Canvas, using default settings, quickstart users, and IAM roles; add users, assign domains, and manage access.

Learn to set up data in an S3 bucket for SageMaker Canvas, including creating or selecting a bucket, configuring settings, uploading csv datasets, and preparing for no-code machine learning.

Explore no-code machine learning with Amazon SageMaker Canvas, navigating data import, model creation, and result analysis to build end-to-end ml workflows without writing code.

Create a no-code model from the banknote authentication dataset in AWS SageMaker Canvas, select the target column class, and build a model achieving 98.6% accuracy for prediction.

Perform single and batch predictions in no-code machine learning with Amazon SageMaker Canvas. The banknote authentication dataset reaches up to 98 percent accuracy with 70/30 splits and downloadable predictions.

Validate batch predictions by comparing predicted and actual data in no-code machine learning with SageMaker Canvas. Achieve high accuracy by building models from training and test data and tuning columns.

Build a no-code spam detection model in Amazon SageMaker Canvas by selecting data and a target column, achieving about 95.4% accuracy to predict test data.

Use no-code machine learning with AWS SageMaker Canvas to predict data from a test dataset, validate accuracy, and view batch or single predictions for spam and legitimate messages.

Leverage no-code machine learning with Amazon AWS SageMaker Canvas to select the target column, preview models, and achieve about 95% accuracy predicting whether a customer will leave the telco.

Learn to perform and validate predictions with no-code machine learning in AWS SageMaker Canvas, achieving around 95.8% accuracy on a customer churn dataset through single and batch predictions.

Build a multi-class model to predict a three-plus category target, using high-impact features like sulfur dioxide, while removing IDs and non-informative ABs, achieving about 67.9% accuracy.

Learn to predict test data with no-code Amazon AWS SageMaker Canvas, achieving about 67.9 percent accuracy and comparing predictions to test data for wine quality.