Utilize the capabilities of Artificial Intelligence and Machine Learning with Splunk and Amazon SageMaker Canvas | AWS Machine Learning Blog

Introduction

As organizations handle increasingly complex data, traditional analytics methods are no longer sufficient. The use of machine learning and artificial intelligence technologies is becoming more prevalent to drive innovation and efficiency at scale.

AI and ML Adoption through AWS

The democratization of AI and ML through AWS and its partners is accelerating adoption across industries. AWS offers a wide range of services and infrastructure to support organizations at every stage of their AI/ML journey.

Data Integration with Splunk and AWS

This section explores how organizations can leverage Amazon SageMaker Canvas and data collected in Splunk to derive actionable insights. It demonstrates the use of generative AI capabilities to enhance data exploration and model building.

Automated Data Engineering Pipeline

The article describes an automated data pipeline using AWS resources to transfer data from Splunk to Amazon S3 for cataloging. This streamlined process enables access to data for various stakeholders through a SQL interface.

ML Model Development with SageMaker Canvas

SageMaker Canvas, a no-code ML development service, empowers users to build and deploy accurate ML models efficiently. The article presents a step-by-step guide on creating a custom ML model for a healthcare use case using SageMaker Canvas.