The Open Frontier

Amazon Bedrock is convenient, but it is a "Closed" system. You can only use the models AWS provides. What if a new, groundbreaking model is released on Hugging Face today, and it’s not in Bedrock yet? Or what if you need to use a small, specialized model (like Mistral 7B) that is cheaper to host yourself?

In this lesson, we master Amazon SageMaker JumpStart and custom Model Deployment for open-source AI.

---

1. Bedrock vs. SageMaker (The Final Comparison)

Factor	Amazon Bedrock	Amazon SageMaker
Model Choice	Limited set (Titan, Claude, Llama).	Infinite (Anything on Hugging Face).
Management	Serverless (No infrastructure).	You manage the EC2 instances.
Price	Pay-per-token.	Pay-per-hour (Idle time costs money).
Control	Standard settings.	Full control over GPU, RAM, and Latency.

---

2. SageMaker JumpStart

SageMaker JumpStart is the "App Store" for AI models. It provides 1-click deployment for models like:

• Llama 3 (Meta)
• Mistral / Mixtral
• Falcon
• Stable Diffusion

The Workflow:

1. Select the model from the JumpStart catalog.
2. Choose your instance type (e.g., ml.g5.2xlarge).
3. Click "Deploy."
4. You get an HTTPS Endpoint that you can call from your application, just like a Bedrock API.

---

3. Deep Learning Containers (DLCs)

For advanced developers, you might want to skip JumpStart and deploy your own container. AWS provides DLCs—Docker images pre-configured with:

• PyTorch / TensorFlow
• NVIDIA CUDA drivers
• Hugging Face libraries

This ensures that your model "Just Works" without you having to manually install complex GPU drivers.

---

4. Multi-Model Endpoints (MME)

If you have 10 different small models (e.g., 10 different specialized translators), it’s expensive to have 10 separate EC2 instances running 24/7.

• The Solution: Use Multi-Model Endpoints.
• You host multiple models on a single SageMaker endpoint.
• SageMaker automatically loads the correct model from S3 into memory when it receives a request for that model ID.

---

5. Industrial Auto-scaling

Unlike Bedrock (which scales behind the scenes), you must configure the scaling for SageMaker.

graph TD
    User[Traffic] --> E[SageMaker Endpoint]
    E --> C[CloudWatch Metric: InvocationsPerInstance]
    C -->|High Traffic| AS[Application Auto Scaling]
    AS -->|Add Instance| E
    
    style AS fill:#e1f5fe,stroke:#01579b

Pro Tip: For GenAI, the best metric to scale on is ConcurrentRequestsPerModel. If too many requests hit one GPU, the latency will skyrocket.

---

6. Pro-Tip: Endpoint "Cold Starts"

SageMaker endpoints take about 5-10 minutes to "Boot up."

• If you are doing a software deployment, use SageMaker Deployment Guardrails (Canary or Linear deployments) to ensure the new model is fully "Warm" before you switch traffic away from the old one.

---

Knowledge Check: Test Your SageMaker Knowledge

---

Summary

SageMaker is the "Pro's Sandbox." It provides the scale and control required for truly custom AI engineering. In the final lesson of this course, we look at the silicon that makes it all possible: Specialized Hardware: Inferentia and Trainium.

---

Next Lesson: Silicon Power: Specialized Hardware (Inferentia and Trainium)