Model development involves the process of creating, training, and refining machine learning models that can generate new content or insights. During model development, data scientists and engineers ingest and prepare datasets, ensuring they are clean and suitable for training. Next, they choose the appropriate algorithms and techniques to build the model. This phase often includes model training, where it learns from the data, adjusting its parameters to improve performance. Once model development is done, the next step is model testing and evaluation to ensure it meets the desired standards before deployment.
