Machine Learning
MLOps Steps for a RAG-Based Application with Llama 3.2, ChromaDB, and Streamlit
Dr Arun Kumar
PhD (Computer Science)MLOps Steps for a RAG-Based Application with Llama 3.2, ChromaDB, and Streamlit
This document outlines the essential MLOps steps for developing a Retrieval-Augmented Generation (RAG) application utilizing Llama 3.2, ChromaDB, and Streamlit. The integration of these technologies allows for efficient data retrieval and enhanced generative capabilities, making it suitable for various applications such as chatbots, content generation, and more. The following sections detail the key steps involved in the MLOps lifecycle, from development to deployment and monitoring.
- Define the Project Scope
- Identify Use Cases: Determine the specific applications of the RAG model, such as customer support, content creation, or data analysis.
- Gather Requirements: Collect functional and non-functional requirements from stakeholders to ensure the application meets user needs.
- Data Collection and Preparation
- Data Sources: Identify and integrate various data sources that will be used for retrieval, such as databases, APIs, or document repositories.
- Data Cleaning: Preprocess the data to remove inconsistencies, duplicates, and irrelevant information.
- Data Annotation: If necessary, annotate the data to improve the performance of the Llama model.
- Model Selection and Training
- Model Selection: Choose Llama 3.2 as the generative model for the application.
- Fine-tuning: Fine-tune Llama 3.2 on domain-specific data to enhance its performance for the intended use case.
- Retrieval Mechanism: Implement ChromaDB to facilitate efficient data retrieval, ensuring it can handle the expected query load.