I'm in the prototype stage and trying to choose between multiple AI models. Beyond factors like accuracy, inference speed, training cost, and scalability, are there any other key considerations I might be overlooking? I'd love to hear your general approach—how do you make these decisions?
What methods do you use when choosing an AI model?
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When selecting a model during the prototype phase, the first step is to clearly define the business problem. For example, are you classifying texts or detecting objects in images? Each use case has different requirements: in one scenario, low latency may be critical (e.g., in a chatbot), while in another, high accuracy may be more important.
My approach involves the second step: checking data suitability. How dependent is the model's performance on the training data? If your data is labeled, you can opt for a model that supports fine-tuning; otherwise, open-source models with zero-shot capabilities may suffice. Finally, you need to consider the deployment environment—whether it will run on a local device or in the cloud. For Edge AI, optimized models (TensorFlow Lite, ONNX Runtime) are preferred, while cloud-focused projects may benefit from API-based solutions (Mistral API, Replicate) for greater flexibility.