Machine Learning Lifecycle

The Machine Learning lifecycle involves several key steps to create an effective model. First, data is collected and prepared, ensuring it’s clean and relevant. Next, the data is used to train the model, allowing it to recognize patterns. The model is then tested to see how well it performs with new, unseen data. If needed, adjustments are made to improve accuracy. Once satisfied, the model is deployed to make predictions or decisions in real-world scenarios. Finally, ongoing monitoring and updating ensure the model remains effective over time as new data becomes available.