Machine learning model deployment is a crucial aspect of the machine learning lifecycle, and implementing efficient MLOps can yield significant benefits. The blog highlights several tools that simplify the deployment process, including Seldon.io, which offers an open-source framework for deploying models in Kubernetes; BentoML, which provides a Python-based architecture for scalable API deployment; TensorFlow Serving, known for its robust system for serving machine learning models; and Kubeflow, which maintains machine learning systems using Kubernetes. Additionally, Cortex offers flexibility in model serving and monitoring across different workflows, while AWS Sagemaker streamlines the machine learning development lifecycle by integrating complex tools and workflows. MLflow organizes the entire ML lifecycle with functions like tracking and model registry, and Torchserve simplifies deploying PyTorch models at scale. Each tool has its pros and cons, with varying levels of complexity, scalability, and platform compatibility, catering to different needs in the ML deployment space.