# What features does Amazon Bedrock offer for model customization or fine-tuning with a user's own data?

Source: https://zilliz.com/ai-faq/what-features-does-amazon-bedrock-offer-for-model-customization-or-finetuning-with-a-users-own-data
Author: zilliz

Amazon Bedrock provides managed tools for customizing foundation models using your data, primarily through fine-tuning and continued pre-training. These features let you adapt models like Amazon Titan or Claude to specific tasks without handling infrastructure. Here’s how it works:

**1. Fine-Tuning Workflows** 
Bedrock supports fine-tuning models by training them further on your dataset. You upload structured data (e.g., labeled examples in CSV/JSON) to Amazon S3, and Bedrock manages the training process. For example, you could fine-tune a text generation model on proprietary medical records to improve its accuracy in clinical summaries. The service optimizes hyperparameters like learning rate automatically, reducing manual effort. Fine-tuned models are deployed via Bedrock’s API, making them accessible like the base models.

**2. Continued Pre-Training** 
For domain-specific adaptation, Bedrock allows continued pre-training. This involves training a base model on large volumes of unstructured data (e.g., legal documents or internal wikis) to improve its grasp of niche terminology or context. A financial firm, for instance, could train a model on SEC filings and earnings reports to enhance its understanding of finance jargon. This process uses techniques like parameter-efficient training (e.g., LoRA) to minimize compute costs while retaining the model’s general knowledge.

**3. Security and Integration** 
Data used for customization is encrypted in transit and at rest, and models are isolated to prevent cross-tenant access. Bedrock integrates with AWS services like CloudWatch for monitoring inference metrics and SageMaker for advanced preprocessing if needed. However, customization options vary by model—some may support full fine-tuning, while others only allow lightweight adaptation. You retain control over custom models, which can be versioned or deleted via the Bedrock console or API.