Posts

AWS Architecture for LLM, GenAI, RAG, and Graph

Image
                                                                      AWS Here's a concise breakdown of what’s in the AWS contact center RAG architecture and modern AWS innovations/tools you can consider adding/enhancing for LLM, GenAI, RAG, and Graph-based use cases: ✅ Current Architecture Summary Core Interaction : Amazon Connect + Lex : Voice/chat → Lex bot AWS Lambda : Fulfillment logic → interacts with LLMs & KB Amazon Bedrock : Claude & Cohere embedding Amazon OpenSearch Serverless : RAG KB indexing Amazon S3 : Document storage Amazon SageMaker : LLM testing CloudWatch + Athena + QuickSight : Analytics, logs, and dashboards 🚀 Modern AWS Additions to Enhance This Architecture 1. Amazon Knowledge Bases for Amazon Bedrock (NEW) Built-in RAG : No manual embedding/indexing ...
Post a Comment
Read more

Explained Fine Tuning Small Language Model

Image
                                                                 generated by Chatgpt Here's a concise Azure-based architecture for fine-tuning a small language model using Hugging Face datasets : 🔷 Azure Architecture for Fine-Tuning Small LLM with Hugging Face Data 1. Data Preparation Layer Azure Blob Storage Store raw Hugging Face datasets ( .json , .csv , etc.) Can integrate directly with Hugging Face datasets.load_dataset() using local download and upload. 2. Compute Layer Azure Machine Learning (AzureML) Workspace Manage training jobs, compute targets, and experiment tracking. AzureML Compute Cluster (GPU) Use NC/T4-v3/ND-series VMs for training (cost-effective for small models). Supports distributed training with Hugging Face + DeepSpeed if needed. 3. Training Environment Custom Doc...
Post a Comment
Read more