AWS Public Sector Blog
Discover how nonprofits can utilize no-code machine learning with Amazon SageMaker Canvas
Nonprofit organizations are on the frontlines of addressing the world’s most pressing challenges and often doing so with limited resources. Machine learning (ML) has emerged as a powerful tool to help nonprofits expedite manual processes, quickly unlock insights from data, and accelerate mission outcomes—from personalizing marketing materials for donors to predicting member churn and donation patterns.
However, the complexity and resource requirements of traditional ML development have historically placed these capabilities out of reach for many nonprofit organizations. Amazon Web Services (AWS) addresses this gap with Amazon SageMaker Canvas, a low-code ML service that simplifies model development and deployment. In this post, we explore how Amazon SageMaker Canvas can make ML accessible and actionable for nonprofit organizations. We’ll highlight key features that allow your nonprofit to harness the power of ML without data science expertise or dedicated engineering teams.
Overview
SageMaker Canvas is a visual, point-and-click service that allows business analysts and data scientists to utilize ready-to-use ML models and build custom ML models to generate accurate predictions without the need to write any code.
Low-code interface lowers bar of entry
The user-friendly, no-code interface of SageMaker Canvas lowers the bar of entry for nonprofits to harness the power of ML. Rather than requiring experienced data scientists, the platform empowers your nonprofit staff with varying technical backgrounds to build and deploy ML models across a variety of data types—from tabular and time-series data to images and text. SageMaker Canvas guides users through the entire ML lifecycle using a point-and-click interface, built-in data preparation tools, and automated model building capabilities.
SageMaker Canvas uses the same technology as Amazon SageMaker to automatically clean and combine your data, measure against hundreds of models under the hood, select the best performing one, and generate new individual or batch predictions. It supports multiple predictive problem types. You can use time-series forecasting to generate donation predictions and make informed decisions on budget plannings. With binary classification, you can predict the likelihood of a donor to churn, and strategize your donor outreach. For a full list of custom model types, check out this documentation.
Figure 1. SageMaker Canvas has a simple and intuitive user interface for you to import data and build custom models
Ready-to-use models for immediate impact
Building high-performing, custom ML models requires significant time and resources. Organizations typically conduct extensive business reviews to validate use cases before investing in ML projects, which often prevents teams from applying ML to ad-hoc tasks or projects with no direct benefit to the mission.
SageMaker Canvas eliminates this barrier with its catalog of pre-trained ML models for common use cases. These models support a range of functionalities: image recognition, natural language processing (NLP), text extraction, to sentiment analysis. By simply uploading your data, you can immediately utilize these pre-trained models and export the results—no model development required.
Here are two examples of how nonprofits can benefit from these pre-trained models:
- Sentiment analysis: Nonprofits can analyze donor feedback from fundraising events more efficiently. Instead of manually reviewing spreadsheets of survey responses, organizations can upload the data to SageMaker Canvas’s sentiment analysis model for instant feedback assessment.
- Expense analysis: Rather than manual receipt entry, your bookkeepers can upload receipt images to the expense analysis model. The model extracts key information including dates, item prices, total amounts, and payment terms automatically.
These pre-trained models allow nonprofit teams to integrate ML into daily operations and decision-making without technical expertise, keeping their focus on mission-critical activities.
Figure 2. SageMaker Canvas provides ready-to-use models for common use cases
Generative AI integrations
SageMaker Canvas includes foundation models (FMs) as part of its ready-to-use models. Whether crafting personalized donor emails, ideating eye-catching social media content, or summarizing an executive report, you can tap into the power of these FMs through an interactive chatbot. You can also connect SageMaker Canvas to your document repository for information retrieval. This enhances efficient knowledge sharing across the organization while supporting informed decision-making at all levels.
To further tailor these AI capabilities to your unique missions, you can fine-tune a FM with SageMaker Canvas. You can train the models using datasets of mission-specific prompts and ideal responses—such as donor engagement strategies, mission-area specific languages, volunteer management scenarios, and program impact assessments—to fine tune your model to generate customized output.
Data integration and preparation
Nonprofit organizations use multiple software-as-a-service (SaaS) products, which results in data residing in disparate systems and formats. Data integration and preparation for analysis often consumes resources that organizations could better spend on their mission. SageMaker Canvas solves this challenge by providing connections to more than 50 sources. These include Amazon Simple Storage Service (Amazon S3), Salesforce Data Cloud, Snowflake, Mailchimp, local file uploads and many more (see the SageMaker Developer Guide for a full list of supported sources).
SageMaker Canvas then provides a visual interface for data preparation through its built-in tools. These tools enable users to join data, remove duplicates, handle missing values, etc. The platform’s Data Wrangler feature includes over 300 data transformations and displays an interactive data flow for managing the preparation pipeline, streamlining data preparation for machine learning.
In addition, you can now leverage Amazon Q Developer in Sagemaker Canvas. Amazon Q Developer, a generative AI assistant integrated with SageMaker Canvas, supports users throughout their machine learning workflow. The assistant provides contextual suggestions, explains results, and offers data transformation rationales through natural language interactions, making data-driven decision-making more accessible.
Figure 3. SageMaker Canvas makes data preparation and cleaning easier with integration of Amazon Q Developer
Cost-effective scaling
Unlike traditional ML platforms that require investments in expensive infrastructure, data science talent, and ongoing maintenance, SageMaker Canvas is designed to be an accessible, pay-as-you-go service. Just like most of the managed AWS services, SageMaker Canvas will automatically provision the compute resources required to train and run your models. This reduces operational overhead for your organization. SageMaker Canvas has you covered with auto-shutdown features, helping you optimize your budget while still driving impactful ML initiatives. This way, you can focus on using machine learning to further your social mission without breaking the bank. For more details on pricing, see Amazon SageMaker Canvas pricing.
Governance and MLops best practices
Amazon SageMaker Canvas helps you implement machine learning Operations (MLOps) best practices to secure your organization’s ML projects. The platform provides granular user-level permissions and single sign-on (SSO) capabilities, protecting sensitive data while simplifying access for staff and volunteers. The Model Registry in SageMaker Canvas enables tracking, documentation, and version control of ML models, ensuring consistent governance. If your organization has other ML toolings, you can export model notebooks and integrate with existing workflows.
Conclusion
In this post, we discussed how your nonprofit organization can harness the power of ML with SageMaker Canvas. To learn more about SageMaker Canvas, explore Amazon SageMaker Canvas Resources. For more hands-on experience, check out this SageMaker Canvas workshop for nonprofits.
To discuss your nonprofit’s needs with AWS experts, complete the AWS Public Sector – Contact Us form for your organization.
Also, you’re invited to join us at the upcoming AWS Summit in Washington, DC on June 10-11, 2025 to learn more about how your organization can accelerate your mission outcomes with AWS AI solutions. Here are some sessions that you may be interested in attending:
- TNC201 – Spotlight Lab: Build a custom model using Amazon SageMaker Canvas
- NPR 202 – Digital democracy in action: How Change.org used AI to increase petition support
- NPR 203 – From big data to dig change: Building AI solutions for global reach
- NPR 207 – Generative AI for nonprofits: Low- to full-code development approaches