Selva kumar Rajendran@selvarkumarr

Training AI/ML models is still largely a game of educated guesswork. Engineers configure hyperparameters, pick hardware, estimate budgets, then launch training jobs that can run for hours or days, only to discover the configuration was wrong. The model didn't converge. It ran out of memory. It cost ten times more than expected. By then, the compute is already spent. There is not a good solution in the market . Tools like TensorBoard and W&B only provides data and analysis on what already happened. But they don't answer the question that actually matters: what's going to happen before you start? PreFlight changes that. It's an AI training intelligence platform that analyzes your project before a single GPU is allocated. Predicting cost, runtime, VRAM requirements, convergence likelihood, and hardware fit. It recommends optimal configurations: the right GPU, batch size, learning rate, precision settings. During training, it monitors health in real time, catching problems before they become expensive failures. And it learns from every completed experiment, getting smarter with each run. It uses trained ML models for duration prediction and cost estimation, plus a fine-tuned Gemma 4B model that interprets complex training configurations and generates actionable recommendations. The platform runs on AMD GPU instances. We fine-tuned a Gemma explainer model using AMD's notebook environment. It's deployable on AMD Developer Cloud or Fireworks AI. Everything is containerized for easy deployment.

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