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The burgeoning field of Artificial Intelligence (AI) is revolutionizing numerous industries, with machine learning (ML) models forming the core of many intelligent systems. However, transitioning effective ML models from development to production environments poses significant challenges. This research investigates the integration of Machine Learning Operations (MLOps) and DevOps principles, leveraging Artificial Intelligence (AI) to automate critical aspects of model deployment, version control, and lifecycle management. By streamlining the entire machine learning workflow, this approach aims to enhance the efficiency, reliability, and governance of AI-powered solutions. The paper commences with a comprehensive overview of MLOps and DevOps, highlighting their distinct yet complementary roles. MLOps encompasses a set of practices designed specifically for the unique challenges associated with the development, deployment, and management of ML models. These challenges include data versioning, model interpretability, performance monitoring, and drift detection. DevOps, on the other hand, focuses on fostering collaboration and communication between development and operations teams within the software development lifecycle. Its core principles of continuous integration/continuous delivery (CI/CD) facilitate rapid application delivery and infrastructure management. The paper then delves into the potential of AI for bridging the gap between MLOps and DevOps. AI techniques hold immense promise for automating various stages of the machine learning workflow. One crucial area of focus is automated model deployment. Traditionally, deploying ML models involves manual configuration and scripting, a time-consuming and error-prone process. AI-powered automation platforms can streamline this process by intelligently selecting target environments, provisioning resources, and configuring infrastructure based on model requirements. This not only reduces deployment time but also minimiz

The burgeoning field of Artificial Intelligence (AI) is revolutionizing numerous industries, with machine learning (ML) models forming the core of many intelligent systems. However, transitioning effective ML models from development to production environments poses significant challenges. This research investigates the integration of Machine Learning Operations (MLOps) and DevOps principles, leveraging Artificial Intelligence (AI) to automate critical aspects of model deployment, version control, and lifecycle management. By streamlining the entire machine learning workflow, this approach aims to enhance the efficiency, reliability, and governance of AI-powered solutions. The paper commences with a comprehensive overview of MLOps and DevOps, highlighting their distinct yet complementary roles. MLOps encompasses a set of practices designed specifically for the unique challenges associated with the development, deployment, and management of ML models. These challenges include data versioning, model interpretability, performance monitoring, and drift detection. DevOps, on the other hand, focuses on fostering collaboration and communication between development and operations teams within the software development lifecycle. Its core principles of continuous integration/continuous delivery (CI/CD) facilitate rapid application delivery and infrastructure management. The paper then delves into the potential of AI for bridging the gap between MLOps and DevOps. AI techniques hold immense promise for automating various stages of the machine learning workflow. One crucial area of focus is automated model deployment. Traditionally, deploying ML models involves manual configuration and scripting, a time-consuming and error-prone process. AI-powered automation platforms can streamline this process by intelligently selecting target environments, provisioning resources, and configuring infrastructure based on model requirements. This not only reduces deployment time but also minimiz

The burgeoning field of Artificial Intelligence (AI) is revolutionizing numerous industries, with machine learning (ML) models forming the core of many intelligent systems. However, transitioning effective ML models from development to production environments poses significant challenges. This research investigates the integration of Machine Learning Operations (MLOps) and DevOps principles, leveraging Artificial Intelligence (AI) to automate critical aspects of model deployment, version control, and lifecycle management. By streamlining the entire machine learning workflow, this approach aims to enhance the efficiency, reliability, and governance of AI-powered solutions. The paper commences with a comprehensive overview of MLOps and DevOps, highlighting their distinct yet complementary roles. MLOps encompasses a set of practices designed specifically for the unique challenges associated with the development, deployment, and management of ML models. These challenges include data versioning, model interpretability, performance monitoring, and drift detection. DevOps, on the other hand, focuses on fostering collaboration and communication between development and operations teams within the software development lifecycle. Its core principles of continuous integration/continuous delivery (CI/CD) facilitate rapid application delivery and infrastructure management. The paper then delves into the potential of AI for bridging the gap between MLOps and DevOps. AI techniques hold immense promise for automating various stages of the machine learning workflow. One crucial area of focus is automated model deployment. Traditionally, deploying ML models involves manual configuration and scripting, a time-consuming and error-prone process. AI-powered automation platforms can streamline this process by intelligently selecting target environments, provisioning resources, and configuring infrastructure based on model requirements. This not only reduces deployment time but also minimiz