Today, I’m excited to introduce a new capability in Amazon SageMaker Canvas to use foundation models (FMs) from Amazon Bedrock and Amazon SageMaker Jumpstart through a no-code experience. This new capability makes it easier for you to evaluate and generate responses from FMs for your specific use case with high accuracy.
Every business has its own set of unique domain-specific vocabulary that generic models are not trained to understand or respond to. The new capability in Amazon SageMaker Canvas bridges this gap effectively. SageMaker Canvas trains the models for you so you don’t need to write any code using our company data so that the model output reflects your business domain and use case such as completing a marketing analysis. For the fine-tuning process, SageMaker Canvas creates a new custom model in your account, and the data used for fine-tuning is not used to train the original FM, ensuring the privacy of your data.
Earlier this year, we expanded support for ready-to-use models in Amazon SageMaker Canvas to include foundation models (FMs). This allows you to access, evaluate, and query FMs such as Claude 2, Amazon Titan, and Jurassic-2 (powered by Amazon Bedrock), as well as publicly available models such as Falcon and MPT (powered by Amazon SageMaker JumpStart) through a no-code interface. Extending this experience, we enabled the ability to query the FMs to generate insights from a set of documents in your own enterprise document index, such as Amazon Kendra. While it is valuable to query FMs, customers want to build FMs that generate responses and insights for their use cases. Starting today, a new capability to build FMs addresses this need to generate custom responses.
To get started, I open the SageMaker Canvas application and in the left navigation pane, I choose My models. I select the New model button, select Fine-tune foundation model, and select Create.
I select the training dataset and can choose up to three models to tune. I choose the input column with the prompt text and the output column with the desired output text. Then, I initiate the fine-tuning process by selecting Fine-tune.
Once the fine-tuning process is completed, SageMaker Canvas gives me an analysis of the fine-tuned model with different metrics such as perplexity and loss curves, training loss, validation loss, and more. Additionally, SageMaker Canvas provides a model leaderboard that gives me the ability to measure and compare metrics around model quality for the generated models.
Now, I am ready to test the model and compare responses with the original base model. To test, I select Test in Ready-to-use models from the Analyze page. The fine-tuned model is automatically deployed and is now available for me to chat and compare responses.
Now, I am ready to generate and evaluate insights specific to my use case. The icing on the cake was to achieve this without writing a single line of code.
PS: Writing a blog post at AWS is always a team effort, even when you see only one name under the post title. In this case, I want to thank Shyam Srinivasan for his technical assistance.