Now it’s time to build your AI model! Selecting the right model to use for your solution may take some research and experimentation grounded in thoughtfully chosen evaluation metrics and thresholds, cost assessment, level of openness, and more. Once you’ve decided on a model, you’ll embark on training, testing, and further evaluation to refine your model until it produces the outputs needed to sufficiently address the problem you’ve defined. In other words, is the model performing at the level you need it to when you provide it with new input data?
When the model is performing satisfactorily according to your chosen evaluation metrics, it’s ready to be deployed and used by the world (mostly likely through a product interface). Like any technology, AI models should be thought of as a living system—always being monitored and evaluated for potential improvements.
PJMF has not developed content for this module yet, but let us know you’re interested, and we’ll keep you in the loop if/when it’s released.
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What people, processes, and technology can help ensure our data is effectively managed throughout its lifecycle?
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From user testing to rapid prototyping, what product development practices will support a successful integration of AI?
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What is our organizational-wide strategy for how AI should be leveraged to further our mission and how will we get there?
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The Patrick J. McGovern Foundation (PJMF) is a philanthropic organization dedicated to advancing artificial intelligence and data science solutions to create a thriving, equitable, and sustainable future for all. PJMF works in partnership with public, private, and social institutions to drive progress on our most pressing challenges, including digital health, climate change, broad digital access, and data maturity in the social sector.
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