Amazon SageMaker is a cloud-based machine learning platform that enables developers to build, train, and deploy machine learning models at scale. One of the key features of Amazon SageMaker is the Model Registry, which allows developers to manage and deploy machine learning models built in Amazon SageMaker Canvas to production.
In this article, we will explore how to use Amazon SageMaker Model Registry to deploy machine learning models built in Amazon SageMaker Canvas to production.
Step 1: Build and Train Your Machine Learning Model in Amazon SageMaker Canvas
The first step in deploying a machine learning model using Amazon SageMaker Model Registry is to build and train your model in Amazon SageMaker Canvas. Amazon SageMaker Canvas is a visual interface that allows developers to build and train machine learning models without writing any code.
To build and train your machine learning model in Amazon SageMaker Canvas, follow these steps:
1. Open the Amazon SageMaker console and select “Notebook instances” from the left-hand menu.
2. Click “Create notebook instance” and follow the prompts to create a new notebook instance.
3. Once your notebook instance is created, open JupyterLab and navigate to the “SageMaker Examples” tab.
4. Select the “Introduction to Amazon SageMaker Studio” example and follow the instructions to build and train your machine learning model.
Step 2: Create a Model Package in Amazon SageMaker
Once you have built and trained your machine learning model in Amazon SageMaker Canvas, the next step is to create a model package in Amazon SageMaker. A model package is a container that includes your trained machine learning model, as well as any dependencies or configuration files required to run the model.
To create a model package in Amazon SageMaker, follow these steps:
1. Open the Amazon SageMaker console and select “Model packages” from the left-hand menu.
2. Click “Create model package” and follow the prompts to create a new model package.
3. In the “Model details” section, select the algorithm and framework used to build your machine learning model.
4. In the “Model artifacts” section, upload the trained machine learning model from Amazon SageMaker Canvas.
5. In the “Environment” section, specify any dependencies or configuration files required to run the model.
6. Click “Create model package” to create your model package.
Step 3: Register Your Model Package in Amazon SageMaker Model Registry
Once you have created your model package in Amazon SageMaker, the next step is to register your model package in Amazon SageMaker Model Registry. Amazon SageMaker Model Registry is a central repository for managing and versioning machine learning models.
To register your model package in Amazon SageMaker Model Registry, follow these steps:
1. Open the Amazon SageMaker console and select “Model registry” from the left-hand menu.
2. Click “Create model” and follow the prompts to create a new model.
3. In the “Model details” section, specify the name and description of your model.
4. In the “Model artifacts” section, select the model package you created in Step 2.
5. Click “Create model” to register your model package in Amazon SageMaker Model Registry.
Step 4: Deploy Your Model to Production
Once you have registered your model package in Amazon SageMaker Model Registry, the final step is to deploy your model to production. Amazon SageMaker provides several options for deploying machine learning models, including Amazon SageMaker endpoints and AWS Lambda functions.
To deploy your model to production using Amazon SageMaker endpoints, follow these steps:
1. Open the Amazon SageMaker console and select “Endpoints” from the left-hand menu.
2. Click “Create endpoint” and follow the prompts to create a new endpoint.
3. In the “Endpoint configuration” section, select the model you registered in Step 3.
4. In the “Production variants” section, specify the number of instances and instance type for your endpoint.
5. Click “Create endpoint” to deploy your model to production.
Conclusion
Amazon SageMaker Model Registry is a powerful tool for managing and deploying machine learning models built in Amazon SageMaker Canvas to production. By following the steps outlined in this article, you can easily build, train, and deploy machine learning models at scale using Amazon SageMaker.
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