AI Books Data Preparation 
Towards AI
NOVEMBER 5, 2024
Author(s): Towards AI Editorial Team Originally published on Towards AI. To make learning LLM development more accessible, we’ve released an e-book second edition version of Building LLMs for Production on Towards AI Academy at a lower price than on Amazon. What’s New? Key Areas of Focus in Building LLMs for Production 1.
NOVEMBER 20, 2024
Retrieval Augmented Generation (RAG) has become a crucial technique for improving the accuracy and relevance of AI-generated responses. Knowledge base – You need a knowledge base created in Amazon Bedrock with ingested data and metadata.
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AWS Machine Learning Blog
NOVEMBER 1, 2024
Fine-tuning is a powerful approach in natural language processing (NLP) and generative AI , allowing businesses to tailor pre-trained large language models (LLMs) for specific tasks. By fine-tuning, the LLM can adapt its knowledge base to specific data and tasks, resulting in enhanced task-specific capabilities.
Snorkel AI
DECEMBER 2, 2024
The integration between the Snorkel Flow AI data development platform and AWS’s robust AI infrastructure empowers enterprises to streamline LLM evaluation and fine-tuning, transforming raw data into actionable insights and competitive advantages. Learn more and apply here or book a meeting at AWS re:Invent 2024.
JULY 3, 2025
Although rapid generative AI advancements are revolutionizing organizational natural language processing tasks, developers and data scientists face significant challenges customizing these large models. Organizations need a unified, streamlined approach that simplifies the entire process from data preparation to model deployment.
JULY 7, 2025
Fine Tuning LLM Models – Generative AI Course When working with LLMs, you will often need to fine-tune LLMs, so consider learning efficient fine-tuning techniques such as LoRA and QLoRA, as well as model quantization techniques.
AWS Machine Learning Blog
MAY 1, 2025
Best practices for data preparation The quality and structure of your training data fundamentally determine the success of fine-tuning. Our experiments revealed several critical insights for preparing effective multimodal datasets: Data structure You should use a single image per example rather than multiple images.
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