Who should use the Train neural networks workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
A streamlined workflow to prepare data, train a neural network model, and evaluate its performance using AI tools.
Deliverable outcome
A portable, documented model file ready for deployment or sharing.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A portable, documented model file ready for deployment or sharing.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use NVIDIA NeMo Data Designer to a clean, split, and normalized dataset ready for model training. Then, you pass the output to Keras to a defined model architecture with initialized weights and training configuration. Then, you pass the output to PyTorch-Ignite to a trained model with stabilized loss curves and acceptable validation performance. Then, you pass the output to scikit-learn to quantitative and qualitative assessment of model generalization on unseen data. Then, you pass the output to Optuna to an optimized model with improved performance metrics. Finally, ONNX (Open Neural Network Exchange) is used to a portable, documented model file ready for deployment or sharing.
Prepare and preprocess the dataset
A clean, split, and normalized dataset ready for model training.
Design and initialize the neural network architecture
A defined model architecture with initialized weights and training configuration.
Train the model with iterative optimization
A trained model with stabilized loss curves and acceptable validation performance.
Evaluate and validate model performance
Quantitative and qualitative assessment of model generalization on unseen data.
Optimize and tune hyperparameters (optional)
An optimized model with improved performance metrics.
Export and save the trained model
A portable, documented model file ready for deployment or sharing.
Collect raw data, clean it (handle missing values, outliers), split into training/validation/test sets, and apply normalization or augmentation. This ensures the model learns from high-quality, representative data.
Why NVIDIA NeMo Data Designer: NVIDIA NeMo Data Designer provides synthetic data generation and data augmentation capabilities, which directly address the need for data preparation and preprocessing including augmentation libraries.
Define the model structure (layers, activation functions, loss function) based on the problem type (classification, regression, etc.). Initialize weights and choose an optimizer and learning rate.
Why Keras: Keras is a high-level neural network API that runs on top of TensorFlow, providing model definition utilities for designing and initializing architectures.
Run forward passes, compute loss, backpropagate gradients, and update weights over multiple epochs. Monitor training/validation loss to detect overfitting and adjust hyperparameters (e.g., batch size, learning rate).
Why PyTorch-Ignite: PyTorch-Ignite provides model training, evaluation, and experiment management, including logging capabilities suitable for iterative optimization.
Compute final metrics on the held-out test set (accuracy, precision, recall, F1, confusion matrix). Analyze errors and visualize predictions to confirm generalization.
Why scikit-learn: scikit-learn provides classification, regression, and clustering metrics that are essential for evaluating model performance.
Use grid search, random search, or Bayesian optimization to find better hyperparameters (learning rate, batch size, architecture depth). Retrain the best configuration and re-evaluate.
Why Optuna: Optuna is specifically designed for hyperparameter search and multi-objective optimization, directly matching the need for hyperparameter tuning.
Serialize the model weights and architecture to a file (e.g., .pt, .h5, .onnx). Optionally convert to a deployment format (TorchScript, TensorFlow SavedModel) for inference.
Why ONNX (Open Neural Network Exchange): ONNX (Open Neural Network Exchange) is specifically designed for model conversion and export, enabling saving models in a portable format.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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