The Moment of Truth

We have prepared the data, selected the model, tuned the prompt, and evaluated the output. Now it is time to move the project from the "Lab" to the "World."

Deployment is the process of making your AI available as a production-ready API. But deployment is not the end; it is the beginning of Monitoring. High-performing AI Practitioners know that a model is a "Living" thing that must be watched constantly.

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1. Deployment Strategies

When updating a model, professional teams don't just "Swap the old for the new" all at once. They use safety strategies:

• Blue/Green Deployment: You keep the old model (Blue) running while you spin up the new model (Green). If the new one works, you move the traffic to it. If it fails, you swap back instantly.
• A/B Testing: You send 50% of your users to Model A and 50% to Model B. You see which one results in higher customer satisfaction before you pick a winner.
• Canary Deployment: You send the new model to only 5% of users (The "Canary in the coal mine"). If it doesn't crash, you slowly ramp up to 100%.

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2. Monitoring: The Three Pillars

Once the model is live, you must monitor three distinct things:

A. Operational Monitor (Service Health)

Is the system up? Is it slow?

• Tool: Amazon CloudWatch.
• Metric: Latency, CPU usage, Error codes (e.g., 500 errors).

B. Business Monitor (Success Metrics)

is the AI actually solving the problem we built it for?

• Metric: "Did the customer find the answer?" or "Did the user click the recommendation?"

C. Data & Model Monitor (The "Brains" Check)

Is the math still correct?

• Tool: SageMaker Model Monitor.
• Metric: Model Drift. (As we learned in Module 10, this is when the real-world data starts to look different from the training data).

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3. Feedback Loops: The Secret Sauce

A great AI project includes a way for the user to give feedback.

• "Was this summary helpful? [Yes/No]"
• This feedback is then collected in an S3 Bucket and used to Re-train the model in the next cycle.

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4. Visualizing the Deployment Lifecycle

graph TD
    A[Final App Development] --> B[Deployment: A/B Test]
    B --> C{Check CloudWatch}
    C -->|High Errors| D[Rollback to Old Version]
    C -->|Smooth Performance| E[Scale to 100%]
    
    E --> F[Continuous Monitoring]
    F -->|Model Drift Detected| G[Feedback Loop: Collect New Data]
    G --> H[Re-start Cycle: Module 13, Lesson 1]

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5. Summary: AI is a Loop, not a Line

The AI Project Lifecycle is ever-ending.

1. You prepare data.
2. You build.
3. You deploy.
4. You learn from the deployment.
5. You start over.

This continuous improvement is what separates successful AI companies from those whose projects eventually rot and become useless.

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Recap of Module 13

We have completed the project journey:

• We understood the 80% Rule (Data Prep).
• We mastered the Trade-offs of Model Selection.
• We implemented Automatic and Human Evaluation.
• We deployed with Safety and installed Monitoring.

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Knowledge Check

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What's Next?

Building the model is done. But how much will it cost? In Module 14: Cost, Performance, and Risk Awareness, we look at the financial and operational trade-offs of the AI cloud.