Streamlining AI Application: MLOps Best Practices and Platform Automation Illustrated through an Advanced RAG based Chatbot

This research study presents a comprehensive guide to MLOps best practices tailored for developing and deploying Artificial Intelligence (AI) based applications, focusing on the challenges and recent techniques in the field. Current systems face challenges such as maintaining model performance over time, ensuring scalability, platform automation, and handling complex deployment processes. Our proposed objective is to address these challenges through a detailed case study of an advanced Retrieval-Augmented Generation (RAG) based chatbot. This paper illustrates the critical components of an efficient Machine Learning Operations (MLOps) pipeline, covering the full life-cycle from data ingestion and model evaluation to continuous integration and deployment. Emphasis is placed on platform automation to streamline setup and operational processes, ensuring scalability, repeatability, and maintainability. Integrating MLOps practices aims to enhance the reliability and performance of AI applications, providing valuable insights and practical guidelines for practitioners in the field.