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Evaluating AI Models, Unlocking Unstructured Data, and Achieving Reliability w/ Ben Kus
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Navigating AI Model Development and Infrastructure Decisions for Startups
Key insights include the importance of change control and management around prompts and model inputs, deployment strategies, customer and data control, as well as test and evaluation in AI model development. When advising startups on choosing between focusing on the control layer aspect or model fit, it is suggested to prioritize knowing the need to train a model before investing in infrastructure. Leveraging model gardens provided by big public cloud providers like GCP, AWS, and Azure can simplify model deployment and configuration changes. Starting with a hosted solution by these cloud technology providers is recommended for simplicity and benefits. Concerning GPU usage, most people are advised not to worry about it initially unless at a large scale. Beginning with existing solutions, whether open source or hosted, and optimizing as needed is prudent. The focus should be on market fit, understanding costs, and learning along the way rather than prematurely investing heavily in infrastructure and model approach.
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