Model Deployment is the process of integrating a machine learning model into a production environment where it can make predictions or decisions based on new data. This step follows the model training phase, where the model learns from historical data. Deployment ensures that the model is accessible to users or applications that need its insights. Once deployed, the model must be monitored and maintained to ensure its performance remains effective over time. This may involve updating the model with new data, retraining it, or adjusting its parameters to adapt to changing conditions. Proper deployment is crucial for maximizing the value of machine learning solutions.