ML Intern in Practice: From Prompt to a Shipped Hugging Face Model 

Most ML projects do not fail because of model choice. They fail in the messy middle: finding the right dataset, checking usability, writing training code, fixing errors, reading logs, debugging weak results, evaluating outputs, and packaging the model for others. This is where ML Intern fits. It is not just AutoML for model selection and […]

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