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Saratix model building process involves a systematic process that combines data, algorithms, and computational power to create intelligent systems capable of performing tasks traditionally requiring human intelligence. The whole process starts off with understanding the task, collecting relevant data, clean and preprocess data, model selection and training, and finally model evaluation.
The whole building AI models is an interactive and ongoing process. It requires continuous learning, improvement, and adaptation based on feedback and changing requirements to create robust and effective AI systems.
Validate and Govern Models
Once the model is built and trained, it will go through a validation process. Saratix will first evaluate the performance of the trained AI model using evaluation metrics that measures its accuracy, precision, F1-score, or other relevant criteria which will then progress to model tuning. We will adjust the hyperparameters and architecture choices to optimize it’s performance.
For the final step, once the model is trained and validated, Saratix will then deploy the tested model for operation in real-world applications, allowing them to generate predictions, recommendations, or perform other tasks.
The AI model will be continuously updated for improvement as new data becomes available or as the requirements of the application evolve. This may involves retraining of the models periodically with fresh data or incorporating new techniques to enhance its performance.