Until now, we have discussed data pipeline, model selection, and the software and hardware parts of machine learning. Now we will dive deeper into strategies for machine learning model deployments. Here, we provide a deep investigation of deployment strategies for machine learning (ML) models by focusing on containerization, orchestration, and automation. We got familiar with these topics in Chapter 4 , and here we dive deeper into them. Here, we start by reviewing the concept of containerization and tools such as Docker to package ML models for scalability and portability. Additionally, we discuss orchestration systems, such as Kubernetes. Kubernetes is examined for its capability in efficiently managing containers. We will provide some practical examples too, to demonstrate how to containerize and orchestrate ML models in practice. This chapter also discusses the design of deployment pipelines and highlights the role of continuous integration (CI) and continuous deployment (CD) tools for automating the deployment process. Practical samples, like automating the deployment of a fraud detection model, show these concepts in practice. In addition, techniques for maintaining and monitoring deployed models are provided. These techniques include tools for detecting and fixing data drift, retraining models, and managing versions. By addressing the most common challenges and providing actionable solutions, this chapter helps you learn efficient and robust deployment techniques for your ML systems.