As we all know, creating an AI/ML model is just the first step toward developing a successful data product. The real challenge lies in integrating the model into the company's systems and workflows, ensuring sustainability and observability, and simplifying the model production process. In this presentation, we will focus on practical solutions to these challenges using cutting-edge technologies such as MLFlow, Azure ML Studio, SageMaker, Databricks, and Kubeflow. We will explore how to integrate AI/ML models with the rest of the IT ecosystem, how to ensure the quality and functionality of the infrastructure, and how to avoid hidden defects and illegal libraries. We will also discuss the best practices for creating a data product using feature stores, frameworks, and custom libraries to simplify the model production process. Furthermore, we will dive into the principles of DevOps and MLOps, discussing how to achieve them in MS Azure environments. We will explore the different environments to use and what to use them for, as well as the tools that can help us achieve these principles. In short, this presentation will provide practical and professional insights into the world of MLOps and DevOps in AI/ML and shows how to create sustainable and observable AI/ML models that integrate seamlessly into the company's systems and workflows.