ishaD
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pm | How to build a generative AI model (11th Oct 25 at 12:34pm UTC)
How to build a generative AI model | | Start with a clear goal and the right data. Define what you want the model to generate (text, images, music, code) and collect a high-quality dataset that matches that goal. Clean and preprocess the data — remove noise, normalize formats, and create train/validation splits — then choose an appropriate architecture (GAN, VAE, diffusion model, or a transformer-based autoregressive model) based on the output type. Enrolling in a generative ai course in pune by SevenMentor helps here because you’ll learn practical techniques for dataset curation, augmentation, and prompt design that dramatically improve model outputs.
Next, design and train the model iteratively. Implement the chosen architecture using frameworks like PyTorch or TensorFlow, set up the training loop, pick suitable loss functions and optimizers, and tune hyperparameters while monitoring metrics (e.g., FID for images, perplexity for text). Use transfer learning or fine-tuning on pre-trained checkpoints when possible to speed up convergence and improve quality. Hands-on sessions in SevenMentor’s generative ai classes in Pune guide you through real training runs, debugging common pitfalls (mode collapse, overfitting) and applying techniques such as regularization, learning-rate schedules, and mixed precision training.
Finally, evaluate, deploy, and maintain the model responsibly. Use both quantitative metrics and human evaluations to validate output quality, then containerize the model (Docker/ONNX) and deploy it behind APIs for inference, adding caching and autoscaling for production loads. Implement monitoring, continuous evaluation on fresh data, and procedures for rollback or retraining as data drifts occur. The generative ai training in Pune from SevenMentor covers deployment best practices, ethical considerations, and MLOps workflows so you can move from prototype to production with confidence. | |
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