LLMs like Llama 2, Flan T5, and Bloom are essential for conversational AI use cases, but updating their knowledge requires retraining, which is time-consuming and expensive. However, with Retrieval Augmented Generation (RAG) using Amazon Sagemaker JumpStart and Pinecone vector database, LLMs can be deployed and kept up to date with relevant information to prevent AI Hallucination.
MLOps is essential for integrating machine learning models into existing systems, and Amazon SageMaker offers features like Pipelines and Model Registry to simplify the process. This article provides a step-by-step implementation for creating custom project templates that integrate with GitHub and GitHub Actions, allowing for efficient collaboration and deployment of ML models.
Spectral clustering is a complex machine learning technique that uncovers patterns in data. Implementing it involves computing affinity and Laplacian matrices, eigenvector embeddings, and performing k-means clustering.