The key stages are: 1. Data preparation: gathering, cleaning, and pre-processing data. 2. Model selection & definition: choosing the right model architecture and setting parameters. 3. Training & iteration: feeding data to the model and adjusting parameters. 4. Validation & evaluation: assessing performance on unseen data. 5. Fine-tuning & optimization: refining the model based on evaluation results. 6. Testing & deployment: final testing and deployment to production.