My Personal Blog

Overview Retrieval-Augmented Generation (RAG) is quickly becoming a standard design pattern for grounding Large Language Models (LLMs) with external knowledge. Instead of relying solely on the training data of the model (which may be outdated or incomplete), RAG retrieves relevant facts from a knowledge base and passes them into the prompt. This ensures responses are…

Overview Incorporating Generative AI (GenAI) into your anomaly detection process can make workflows more adaptive, context-aware, and capable of reasoning. This project showcases how GenAI can be leveraged to create predictive maintenance workflows using Amazon Bedrock. Retrieval Augmented Generation (RAG) with knowledge bases With RAG (Retrieval-Augmented Generation), you can enhance foundation models using company-specific data…

Overview AWS offers 20+ AI services, one of them is Amazon Rekognition. I develop a data science template using Amazon Rekognition to demonstrate how an ML-Ops framework integrates with managed AI services. Packaged as a CloudFormation stack and deployed via AWS Service Catalog, it delivers a fully integrated, end-to-end CI/CD pipeline for image classification and…

Overview This template is a starting point for building and operating Reinforcement Learning (RL) training and serving on AWS. Using demand and profit optimization as examples, it implements a Deep RL workflow to illustrate the end-to-end ML-Ops framework—covering orchestration, data pipelines, training, evaluation and deployment. The document discusses RL at a high level, but its…

Overview This post summarizes the user journey for leveraging the ML-Ops framework within SageMaker Studio. The framework is built on the AWS sample project MLOPS-SM-PROJECT-TEMPLATE-RT. The solution architecture is illustrated below: This sample project features a self-mutating CDK pipeline (shown below) that deploys a Service Catalog application to the client’s account. The Service Catalog provides…

Overview The AWS Sagemaker AI framework uses CodePipeline as the workflow orchestrator. It manages tasks such as building, deploying, testing, preparing data, training models, running inferences, and evaluating results. We define this pipeline using Python CDK in a CloudFormation template, and it operates within the same AWS account. The above diagram illustrates the following sequence:1….