When discussing Machine Learning, many people immediately think of model training. However, the journey of an AI model does not end with deployment. It is essential to recognize that a model's lifecycle is ongoing and requires continuous attention.
Establishing a sustainable Machine Learning lifecycle involves regular updates, monitoring, and improvements to ensure that models remain effective and relevant over time.
This approach not only enhances the performance of AI models but also helps in adapting to new data and changing environments, ultimately leading to better outcomes.