Machine Learning Orchestration in Cloud Environments: Automating the Training and Deployment of Distributed Machine Learning AI Model

The rapid advancement of machine learning (ML) and artificial intelligence (AI) has created an increasing demand for efficient and automated processes in training and deploying AI models. In cloud environments, where vast computational resources are available, orchestrating the entire lifecycle of machine learning workflows becomes crucial to leverage the scalability and flexibility offered by the cloud infrastructure. This research study proposes a novel system architecture and simulation model for machine learning orchestration in cloud environments, aiming to automate the training and deployment of using Distributed Machine Learning (DML) AI model. The proposed system architecture consists of three key components: Job Manager, Resource Manager, and Model Repository. The Job Manager handles the scheduling and coordination of machine learning tasks, ensuring efficient resource allocation and utilization. The Resource Manager dynamically manages the allocation and provisioning of computing resources based on workload demands. The Model Repository acts as a centralized repository for storing and versioning AI models, enabling seamless model deployment and updates. To evaluate the effectiveness and performance of the proposed system architecture, a simulation model is developed. The simulation model provides a virtual environment that mimics real-world cloud scenarios, allowing for extensive experimentation and analysis. Various performance metrics such as training time, resource utilization, and scalability are measured and compared against baseline approaches to demonstrate the superiority of the proposed system architecture. The simulation results indicate that the machine learning orchestration system in the cloud environment significantly improves the efficiency and automation of training and deploying Distributed Machine Learning (DML) AI model. The proposed architecture optimizes resource allocation, minimizes training time, and enhances scalability, leading to cost savings and increased productivity. Moreover, the simulation model provides valuable insights into the behaviour and performance of the system under different workload scenarios, facilitating the fine-tuning and optimization of the orchestration process.