Fine-Tuning vs Continued Pre-Training vs LoRA | Amazon Bedrock Generative AI Explained

In this video, I am sharing what I learned about customizing Foundation Models in Generative AI. Foundation Models already have a lot of general knowledge, but sometimes we need a model to work differently for a specific company, task, or type of application. That is where techniques like Fine-Tuning, Continued Pre-Training, and Low-Rank Adaptation (LoRA) become useful. I tried to explain these concepts in simple language with practical examples because these topics can look complicated when we first start learning about them. Some of the main things I discuss in this video are: Why we may need to customize a Foundation Model What Fine-Tuning actually means How Fine-Tuning can improve model behavior and response style A simple technical-support Fine-Tuning example How custom models can be created with Amazon Bedrock How training data can be stored in Amazon S3 What Fine-Tuning training data may look like Using customization with text and image models Cost and security considerations What Continued Pre-Training means Difference between labeled and unlabeled training data What Low-Rank Adaptation (LoRA) is Why LoRA can require fewer trainable parameters How LoRA keeps the original base model unchanged When Fine-Tuning, Continued Pre-Training, or LoRA may be useful Why sometimes Prompt Engineering or RAG may be a better option One simple idea that helped me understand LoRA is to think about a large Foundation Model like a big textbook. Instead of rewriting the whole textbook for one new task, LoRA is more like adding a smaller set of special notes that work together with the original textbook. The original knowledge stays there, but we can adapt how the model works for a particular requirement. I also talk about an important difference between model customization and RAG. With customization, we are adapting the model itself. With RAG, we can keep information outside the model and provide the required information when the user asks a question. This can be especially helpful when documents or information change frequently. I created this presentation mainly for my own AWS and Generative AI learning so I can understand these concepts more clearly and also practice explaining technical topics in simple words. A very special thank you to Professor Dr. Victor Govindaswamy for his continuous guidance, support, and encouragement throughout my learning. His explanations and feedback have helped me understand AWS concepts in a much better way and encouraged me to look beyond only definitions and understand how these technologies can actually be used in real applications. I really appreciate the time, knowledge, and support he provides to students while we are learning these new and advanced technologies. Thank you for watching my video. I hope my explanation also helps someone who is starting to learn about AWS Generative AI and Foundation Model customization. If you found it useful, you can Like, Share, and Subscribe. Presented by: Akash Patel Concordia University Chicago #AWS #GenerativeAI #AmazonBedrock #AWSBedrock #FineTuning #LoRA #LowRankAdaptation #ContinuedPreTraining #FoundationModels #FoundationModel #ArtificialIntelligence #MachineLearning #AI #AWSGenerativeAI #AmazonWebServices #AWSCertification #AWSLearning #LearnAWS #AWSForBeginners #GenerativeAIForBeginners #AmazonS3 #CustomModels #BedrockCustomModels #LLM #LargeLanguageModels #AIModels #ModelTraining #ModelCustomization #PromptEngineering #RAG #RetrievalAugmentedGeneration #KnowledgeBase #CloudComputing #CloudAI #AIDeveloper #AWSDeveloper #TechLearning #ComputerScience #StudentLearning #LearnGenerativeAI #AITutorial #AWSTutorial #CloudTechnology