Deploying DeepSeek-R1 on Amazon SageMaker for Scalable AI Solutions

Amazon SageMaker JumpStart is a repository for machine learning (ML) users housing foundation models (FMs), algorithms and a variety of ML products that can be instantly executed upon a single click. Available in SageMaker JumpStart is DeepSeek-R1 allowing seamless incorporation into an ML workflow.

This tutorial helps users in deploying DeepSeek-R1 with Amazon SageMaker JumpStart, enabling SageMaker AI tools for performance optimization and configuring endpoints for inference.

1. Ready to Use ML Models: Execute models in a matter of minutes.
2. Hosted: The model runs in AWS infrastructure.
3. Elasticity: Scales up or down as needed.
4. Integration With Other Solutions: Supports imports from AWS AI and ML services like SageMaker Pipelines and Debugger.

• SageMaker Jumpstart is enabled on the AWS Account.
• IAM Permissions ideal for deploying prebuilt models.
• Data and logs will be stored in an Amazon S3 Bucket.
• You will need programmatic access via AWS SDK or CLI.

Step 1: Discover the Model

1. Launch Amazon SageMaker AI Console.
2. Open SageMaker Studio.
3. Click on JumpStart.
4. Locate DeepSeek-R1 in the All Public Models page using the search option.

Step 2: Deploy the Model

1. Choose the model DeepSeek-R1 from the list provided.
2. Hit Deploy, and this will create an inference endpoint with the default settings.
3. Wait until the endpoint has a status of InService.
4. Make inference requests through the API call to the model endpoint.

SageMaker has a pay per use policy so optimizing cost is important.

• ml.m5.large ($0.10/hr) – Best for testing and development.
• ml.g5.2xlarge ($0.75/hr) – Ideal for AI model inference.
• ml.p4d.24xlarge ($32.00/hr) – Optimized for very high workload performance.

Cost Optimization Policies

• Avoid over provisioning using Auto Scaling.
• Use a less expensive AWS region such as Ohio instead of North Virginia.
• Use Batch Requests to increase what can be obtained through each API call.

• SageMaker Pipelines: Build and manage machine learning workflows in a more automated fashion.
• SageMaker Debugger: Analyze and track performance of the model.
• Container Logs: Monitor execution of the model and consumption of resources.

VPC restrictions are enforced through the user's data privacy controls. All client data is secured in an AWS VPC. ApplyGuardrail API can be accessed from SageMaker JumpStart and enables users to use enterprise level security measures to protect usage of FMs.

This deployment strategy enables users to rapidly integrate advanced ML models into SageMaker JumpStart without compromising security or performance. Users can directly manage and adjust performance with the embedded SageMaker AI tools and stay compliant.

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