Deploying machine learning (ML) models is essential for transforming AI projects into functional applications, with key considerations including infrastructure, scalability, latency, and model performance. The process involves evaluating the deployment environment to ensure it meets the necessary computational requirements and adheres to security and compliance standards. Cost management is also crucial, as expenses can increase with resource consumption and inference requests. Platforms like Cerebrium, which offers serverless AI infrastructure, can simplify this process by providing autoscaling capabilities, built-in monitoring, and compliance with standards like GDPR and HIPAA. The text includes a tutorial on deploying a sentiment analysis model using Cerebrium, highlighting the ease of creating an API endpoint and monitoring the application's performance through a dashboard.