Amazon BedrockでAmazon Novaモデルをカスタマイズ可能に

Today, we’re sharing how Amazon Bedrock makes it straightforward to customize Amazon Nova models for your specific business needs. As customers scale their AI deployments, they need models that reflect proprietary knowledge and workflows — whether that means maintaining a consistent brand voice in customer communications, handling complex industry-specific workflows or accurately classifying intents in a high-volume airline reservation system. Techniques like prompt engineering and Retrieval-Augmented Generation (RAG) provide the model with additional context to improve task performance, but these techniques do not instill native understanding into the model.

Amazon Bedrock supports three customization approaches for Nova models: supervised fine-tuning (SFT), which trains the model on labeled input-output examples; reinforcement fine-tuning (RFT), which uses a reward function to guide learning toward target behaviors; and model distillation, which transfers knowledge from a larger teacher model into a smaller, faster student model. Each technique embeds new knowledge directly into the model weights, rather than supplying it at inference time through prompts or retrieved context. With these approaches, you get faster inference, lower token costs, and higher accuracy on the tasks that matter most to your business. Amazon Bedrock manages the training process automatically, requiring only that you upload your data to Amazon Simple Storage Service (Amazon S3) and initiate the job through the AWS Management Console, CLI, or API. Deep machine learning expertise is not required. Nova models support on-demand invocation of customized models in Amazon Bedrock. This means you pay only per-call at the standard rate for the model, instead of needing to purchase more expensive allocated capacity (Provisioned Throughput).

In this post, we’ll walk you through a complete implementation of model fine-tuning in Amazon Bedrock using Amazon Nova models, demonstrating each step through an intent classifier example that achieves superior performance on a domain specific task. Throughout this guide, you’ll learn to prepare high-quality training data that drives meaningful model improvements, configure hyperparameters to optimize learning without overfitting, and deploy your fine-tuned model for improved accuracy and reduced latency. We’ll show you how to evaluate your results using training metrics and loss curves.

Understanding fine-tuning and when to use it

Context-engineering techniques such as prompt engineering or Retrieval-Augmented Generation (RAG) place information into the model’s prompt. These approaches offer significant advantages: they take effect immediately with no training required, allow for dynamic information updates, and work with multiple foundation models without modification. However, these techniques consume context window tokens on every invocation, which can increase cumulative costs and latency over time. More importantly, they do not generalize well. The model is simply reading instructions each time rather than having internalized the knowledge, so it can struggle with novel phrasings, edge cases, or tasks that require reasoning beyond what was explicitly provided in the prompt. Customization techniques, by comparison, incorporate the new knowledge directly into the model by adding an adapter matrix of additional weights and customizing those (“parameter-efficient fine-tuning”, aka “PEFT”). The resulting customized model has acquired new domain-specific skills. Customization allows faster and more efficient small models to reach performance comparable to larger models in the specific training domain.

When to fine-tune: Consider fine-tuning when you have a high-volume, well-defined task where you can assemble quality labeled examples or a reward function. Use cases include training a model to correctly render your company’s logo, embedding brand tone and company policies into the model, or replacing a traditional ML classifier with a small LLM. For example, Amazon Customer Service customized Nova Micro for specialized customer support to improve accuracy and reduce latency, improving accuracy by 5.4% on domain-specific issues and 7.3% on general issues.

Fine-tuned small LLMs like Nova Micro are increasingly replacing traditional ML classifiers for tasks such as intent detection. They deliver the flexibility and world knowledge of an LLM at the speed and cost of a lightweight model. Unlike classifiers, LLMs handle natural variation in phrasing, slang, and context without retraining, and fine-tuning sharpens their accuracy further for the specific task. We demonstrate this with an intent classifier example later in this blog.

When NOT to fine-tune: Fine-tuning requires assembling quality labeled data or a reward function and executing a training job, which involves upfront time and cost. However, this initial investment can reduce per-request inference costs and latency for high-volume applications.

Customization approaches

Amazon Bedrock offers three customization approaches for Nova models:

• Supervised fine-tuning (SFT) customizes the model to learn patterns from labeled data that you supply. This post demonstrates this technique in action.

• Reinforcement fine-tuning (RFT) takes a different approach, using training data combined with a reward function, either custom code or an LLM acting as a judge, to guide the learning process.

• Model distillation, for scenarios requiring knowledge transfer, lets you compress insights from large teacher models into smaller, more efficient student models suitable for resource-constrained devices.

Amazon Bedrock automatically uses parameter efficient fine-tuning (PEFT) techniques appropriate to the model for customizing Nova models. This reduces memory requirements and accelerates training compared to full fine-tuning, while maintaining model quality. Having established when and why to use fine-tuning, let’s explore how Amazon Bedrock simplifies the implementation process, and which Nova models support this customization approach.

Understanding Amazon Nova models on Amazon Bedrock

Amazon Bedrock fully automates infrastructure provisioning, compute management, and training orchestration. You upload data to S3 and start training with a single API call, without managing clusters and GPUs or configuring distributed training pipelines. It provides clear documentation for data preparation (including format specifications and schema requirements), sensible hyperparameter defaults (such as epochCount, learningRateMultiplier), and training visibility through loss curves that help you monitor convergence in real-time.

Nova Models: Several of the Nova models allow fine-tuning (see documentation). After training is completed, you have the option to host the customized Nova models on Amazon Bedrock using cost-effective On Demand inference, at the same low inference price as the non-customized model.

Nova 2 Lite, for example, is a fast, cost-effective reasoning model. As a multimodal foundation model, it processes text, images, and video within a 1-million token context window. This context window supports analysis of documents longer than 400 pages or 90-minute videos in a single prompt. It excels at document processing, video understanding, code generation, and agentic workflows. Nova 2 Lite supports both SFT and RFT.

The smallest Nova model, Nova Micro, is also particularly useful because it offers fast, low-cost inference with LLM intelligence. Nova Micro is ideal for pipeline processing tasks done as part of a larger system, such as fixing addresses or extracting data fields from text. In this post, we show an example of customizing Nova Micro for a segmentation task instead of building a custom data science model.This table shows both Nova 1 and Nova 2 reasoning models and their current availability as of publication time, with which models currently allow RFT or SFT. These capabilities are subject to change; see the online documentation for the most current model availability and customization, and the Nova Users Guide for more detail on the models.

Model

Capabilities

Input

Output

Status

Bedrock fine-tuning

Nova Premier

Most capable model for complex tasks and teacher for model distillation

Text, images, video (excluding audio)

Text

Generally available

Can be used as a teacher for model distillation

Nova Pro

Multimodal model with best combination of accuracy, speed, and cost for a wide range of tasks

Text, images, video

Text

Generally available

SFT

Nova 2 Lite

Low cost multimodal model with fast processing

Text, images, video

Text

Generally available

RFT, SFT

Nova Lite

Low cost multimodal model with fast processing

Text, images, video

Text

Generally available

SFT

Nova Micro

Lowest latency responses at low cost.

Text

Text

Generally available

SFT

Now that you understand how Nova models support fine-tuning through the Amazon Bedrock managed infrastructure, let’s examine a real-world scenario that demonstrates these capabilities in action.

Use case example – intent detection (replacing traditional ML models)

Intent detection determines the category of the user’s intended interaction from the input case. For example, in the case of an airline travel assistance system, the user might be attempting to get information about a previously booked flight or asking a question about airline services, such as how to transport a pet. Often systems will want to route the inquiry to specific agents based on intent. Intent detection systems must operate quickly and economically at high volume.

The traditional solution for such a system has been to train a machine-learning model. While this is effective, developers are more often turning to small LLMs for these tasks. LLMs offer more flexibility, can quickly be modified through prompt changes, and come with extensive world knowledge built in. Their understanding of shorthand, texting slang, equivalent words, and context can provide a better user experience, and the LLM development experience is familiar for AI engineers.

For our example, we will customize Nova Micro model on the open-source Airline Travel Information System (ATIS) data set, an industry standard benchmark for intent-based systems. Nova Micro achieves 41.4% on ATIS with no customization, but we can customize it for the specific task, improving its accuracy to 97% with a simple training job.

Technical implementation: Fine-tuning process

The two critical factors that drive model fine-tuning success are data quality and hyperparameter selection. Getting these right determines whether your model converges efficiently or requires costly retraining. Let’s walk through each component of the implementation process, starting with how to prepare your training data.

Data preparation

Amazon Bedrock requires JSONL (JavaScript Object Notation Lines) format because it supports efficient streaming of large datasets during training, so that you can process your data incrementally without memory constraints. This format also simplifies validation. Each line can be checked independently for errors. Verify that each row in the JSONL file is valid JSON. If the file format is invalid, the Amazon Bedrock model creation job will fail with an error. For more detail, see the documentation on Nova model fine-tuning. We used a script to format the ATIS dataset as JSONL. Nova Micro accepts a separate validation set so we then off split 10% of the data into a validation set (Nova 2 models do this automatically in customization). We also reserved a test set of records, which the model was not trained on, to facilitate clean testing results.

For our intent classifier example, our input data is text only. However, when fine-tuning multimedia models, also make sure you are using only supported image formats (PNG, JPEG, and GIF). Make sure your training examples span the important cases. Validate your dataset with your team and remove ambiguous or contradictory answers before fine-tuning.

{"schemaVersion": "bedrock-conversation-2024", "system": [{"text": "Classify the intent of airline queries. Choose one intent from this list: abbreviation, aircraft, aircraft+flight+flight_no, airfare, airfare+flight_time, airline, airline+flight_no, airport, capacity, cheapest, city, distance, flight, flight+airfare, flight_no, flight_time, ground_fare, ground_service, ground_service+ground_fare, meal, quantity, restriction\n\nRespond with only the intent name, nothing else."}], "messages": [{"role": "user", "content": [{"text": "show me the morning flights from boston to philadelphia"}]}, {"role": "assistant", "content": [{"text": "flight"}]}]}

*Prepared row in a training data sample (note that although it appears wrapped, JSONL format is really a single row per example)*

Important: Note that the system prompt appears in the training data. It is important that the system prompt used for training match the system prompt used for inference, because the model learns the system prompt as context that triggers its fine-tuned behavior.

Data privacy considerations:

When fine-tuning with sensitive data:

• Anonymize or mask PII (names, email addresses, phone numbers, payment details) before uploading to Amazon S3.

• Consider data residency requirements for regulatory compliance.

• Amazon Bedrock does not use your training data to improve base models.

• For enhanced security, consider using Amazon Virtual Private Cloud (VPC) endpoints for private connectivity between S3 and Amazon Bedrock, eliminating exposure to the public internet.

Key hyperparameters

Hyperparameters control the training job. Amazon Bedrock sets reasonable defaults, and you can often use them with no adjustment, but you might need to adjust them for your fine-tuning job to achieve your target accuracy. Here are the hyperparameters for the Nova understanding models – consult the documentation for other models:

Three hyperparameters control your training job’s behavior, and while Amazon Bedrock sets reasonable defaults, understanding them helps you optimize results. Getting these settings right can save you hours of training time and minimize compute costs.

The first hyperparameter, epochCount, specifies how many complete passes the model makes through your dataset. Think of it like reading a book multiple times to improve comprehension. After the first read you might retain 60% of the material; a second pass raises comprehension to 80%. However, after you understand 100% of the material, additional readings waste training time without producing gains. Amazon Nova models support 1 to 5 epochs with a default of 2. Larger datasets typically converge with fewer epochs, while smaller datasets benefit from more iterations. For our ATIS intent classifier example with ~5000 combined samples, we set epochCount to 3.

The learningRateMultiplier controls how aggressively the model learns from errors. It is essentially the step size for corrections. If the learning rate is too high, you might miss details and jump to wrong conclusions. If the rate is too low, you form conclusions slowly. We use 1e-5 (0.00001) for the ATIS example, which provides stable, gradual learning. The learningRateWarmupSteps parameter gradually increases t