Aashmeet Kalra

Create high-quality datasets for training machine learning models. Amazon SageMaker Ground Truth is an end-to-end data labeling service providing workforce management and built-in workflows.

In this workshop, learn how to develop a full ML project end to end with Amazon SageMaker. Start with data exploration and analysis, data cleansing, and feature engineering with SageMaker Data Wrangler.

In this workshop, get hands-on with developer tools on AWS including AWS IDE toolkits, SDKs, and CLIs to build a modern application. Learn how to easily and efficiently build, test, and debug a serverless application and explore modern tooling, including Amazon CodeGuru, AWS SAM tooling, and managed environments, to rapidly prototype and debug secure applications in the cloud.

Managing compute instances to view, run, or share a notebook is tedious. Amazon SageMaker offers several choices to use Jupyter notebooks, including Amazon SageMaker Studio.

Amazon SageMaker Ground Truth makes it easy to quickly label high-quality, accurate training datasets. In this workshop, we set up labeling jobs for text and images to help you understand how to make the most of Amazon SageMaker Ground Truth.