Amazon announced the integration of Amazon DocumentDB (with MongoDB compatibility) with Amazon SageMaker Canvas, enabling users to develop generative AI and machine learning models without coding. This integration simplifies analytics on unstructured data, removing the need for data engineering and science teams. The post details steps to implement and utilize the solution within SageMaker Canvas.
Integrating Amazon DocumentDB with SageMaker Canvas for AI and ML Solutions
Solution Overview
Amazon DocumentDB has integrated with SageMaker Canvas, enabling businesses to leverage generative AI and machine learning (ML) without writing code. You can bring data from Amazon DocumentDB into SageMaker Canvas to build ML models for predictive analytics. This integration simplifies data preparation and analysis, reducing dependency on data engineering and data science teams.
Prerequisites
To implement this solution, you will need AWS Cloud admin access and an AWS Identity and Access Management (IAM) user. You should also complete the environment setup using AWS CloudFormation to deploy Amazon DocumentDB into a VPC.
Implementing the Solution
The following steps detail the process of using Amazon DocumentDB data to build ML models in SageMaker Canvas:
- Create an Amazon DocumentDB connector in SageMaker Canvas.
- Analyze data using generative AI.
- Prepare data for machine learning.
- Build a model and generate predictions.
Conclusion
This integration between Amazon DocumentDB and SageMaker Canvas empowers business analysts to quickly build high-quality ML models without writing code. With a visual interface and no-code ML capabilities, this solution streamlines the entire process, from importing data to model building. To start your low-code/no-code ML journey, refer to Amazon SageMaker Canvas.
