Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. SageMaker Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity.
Amazon SageMaker Canvas is a powerful no-code ML tool designed for business and data teams to generate accurate predictions without writing code or having extensive ML experience. With its intuitive visual interface, SageMaker Canvas simplifies the process of loading, cleansing, and transforming datasets, and building ML models, making it accessible to a broader audience.
However, as your ML needs evolve, or if you require more advanced customization and control, you may want to transition from a no-code environment to a code-first approach. This is where the seamless integration between SageMaker Canvas and SageMaker Studio comes into play.
In this post, we present a solution for the following types of users:
The utility of the solutions proposed in this post is two-fold. Firstly, by demonstrating how you can share models across SageMaker Canvas and SageMaker Studio, non-ML and ML experts can collaborate across their preferred environments, which might be a no-code environment (SageMaker Canvas) for non-experts and a high-code environment (SageMaker Studio) for experts. Secondly, by demonstrating how to share a model from SageMaker Canvas to SageMaker Studio, we show how ML experts who want to pivot from a LCNC approach for development to a high-code approach for production can do so across SageMaker environments. The solution outlined in this post is for users of the new SageMaker Studio. For users of SageMaker Studio Classic, see Collaborate with data scientists for how you can seamlessly transition between SageMaker Canvas and SageMaker Studio Classic.
To seamlessly transition between no-code and code-first ML with SageMaker Canvas and SageMaker Studio, we have outlined two options. You can choose the option based on your requirements. In some cases, you might decide to use both options in parallel.
The following phases describe the steps for collaboration:
Let’s look at the two options (model registry and notebook export) within each step in detail.
Before you dive into the solution, make sure you have signed up for and created an AWS account. Then you need to create an administrative user and a group. For instructions on both steps, refer to Set Up Amazon SageMaker Prerequisites. You can skip this step if you already have your own version of SageMaker Studio running.
Complete the prerequisites for setting up SageMaker Canvas and create the model of your choice for your use case.
The SageMaker Canvas user shares the model with the SageMaker Studio user by either registering it in SageMaker Model Registry, which triggers a governance workflow, or by downloading the full notebook from SageMaker Canvas and providing it to the SageMaker Studio user.
To deploy using SageMaker Model Registry, complete the following steps:
You can now see the model is registered.
You can also see the model is pending approval.
To deploy using a SageMaker notebook, complete the following steps:
You can now share the S3 URI with the SageMaker Studio user.
The SageMaker Studio user accesses the shared model through the model registry to review its details and metrics, or they can import the exported notebook into SageMaker Studio and use Jupyter notebooks to thoroughly validate the model’s code, logic, and performance.
To use the model registry, complete the following steps:
You can review the model details and see that the status is pending.
You can also review the different metrics to check on the model performance.
You can view the model metrics; however, there is limited visibility on the model code and architecture. If you want complete visibility of the model code and architecture with the ability to customize and enhance the model, use the notebook export option.
To use the notebook export option as the SageMaker Studio user, complete the following steps.
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): After a comprehensive review, the SageMaker Studio user can make an informed decision to either approve or reject the model in the model registry based on their assessment of its quality, accuracy, and suitability for the intended use case.
For users who registered their model via the Canvas UI, please follow the below steps to approve the model. For users who exported the model notebook from the Canvas UI, you may register and approve the model using SageMaker model registry, however, these steps are not required.
As the SageMaker Studio user, when you’re comfortable with the model, you can update the status to approved. Approval happens only in SageMaker Model Registry. Complete the following steps:
Now you can see the model is approved.
Once the model is ready to deploy (it has received necessary reviews and approvals), users have two options. For users who took the model registry approach, they can deploy from either SageMaker Studio or from SageMaker Canvas. For users who took the model notebook export approach, they can deploy from SageMaker Studio. Both deployment options are detailed below.
The SageMaker Studio user can deploy the model from the JupyterLab space.
After the model is deployed, you can navigate to the SageMaker console, choose Endpoints under Inference in the navigation pane, and view the model.
Alternatively, if the deployment is handled by the SageMaker Canvas user, you can deploy the model from SageMaker Canvas.
After the model is deployed, you can navigate to the Endpoints page on the SageMaker console to view the model.
To avoid incurring future session charges, log out of SageMaker Canvas.
To avoid ongoing charges, delete the SageMaker inference endpoints. You can delete the endpoints via the SageMaker console or from the SageMaker Studio notebook using the following commands:
Previously, you could only share models to SageMaker Canvas (or view shared SageMaker Canvas models) in SageMaker Studio Classic. In this post, we showed how to share models built in SageMaker Canvas with SageMaker Studio so that different teams can collaborate and you can pivot from a no-code to a high-code deployment path. By either using SageMaker Model Registry or exporting notebooks, ML experts and non-experts can collaborate, review, and enhance models across these platforms, enabling a smooth workflow from data preparation to production deployment.
For more information about collaborating on models using SageMaker Canvas, refer to Build, Share, Deploy: how business analysts and data scientists achieve faster time-to-market using no-code ML and Amazon SageMaker Canvas.
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