Model Development

Data Intelligence Established

Model Development involves defining the model architecture, selecting appropriate algorithms, and training the model using the prepared data. This stage includes feature engineering and selection, splitting the data into training, validation, and testing sets, and fine-tuning the model to achieve optimal performance. The model’s performance is evaluated using relevant metrics, and validation and testing are performed to ensure robustness and generalization. The model development process is documented, and version control is maintained for reproducibility and future reference. Model Development is the core stage where the machine learning model is built and refined to solve the business problem at hand.

  • Architecture Design and Algorithm Selection
  • Feature Engineering, Selection, and Data Preprocessing
  • Training, Fine-tuning, and Optimization
  • Evaluation, Validation, and Testing
  • Robustness and Stress Testing