Build or Buy?

In the world of AWS, you almost always have two ways to solve an AI problem.

1. You can use a Managed Service (like Amazon Rekognition).
2. You can build a Custom Model in Amazon SageMaker.

Choosing the wrong one can lead to "Over-Engineering" (spending $100,000 to save $1,000) or "Under-Engineering" (using a generic tool that isn't accurate enough for your unique business).

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1. Managed AI Services: The "Buy" Option

Managed services are "Black Boxes." AWS has already done the hard work: they collected the data, they hired the PhDs, they trained the model, and they host the servers.

Why Choose Managed?

• Speed to Market: You can launch an AI feature in literally 10 minutes.
• No ML Expertise Required: Your web developers can use it without knowing what a "Neural Network" is.
• Lower Upfront Cost: No expensive training time on GPUs. You only pay for what you use (per image, per word).

When to use Managed Services:

• For Common Tasks: Sentiment analysis, standard object detection (cars, trees), basic translation.
• For Standard Data: If your data is "typical" (standard English, standard JPG photos), a managed service is incredibly accurate.

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2. Custom ML (SageMaker): The "Build" Option

SageMaker is an "End-to-End" platform for the entire machine learning lifecycle. It gives you the "Ingredients" and the "Kitchen."

Why Choose Custom?

• Niche Domain Expertise: If you are analyzing specialized data (e.g., medical X-rays, ultra-precision satellite imagery, or rare languages), a generic AWS service will fail. You need a custom model.
• Full Control: You control the architecture, the weights, and the deployment environment.
• Competitive Advantage: If your "Special Sauce" is a unique algorithm, you can't use an API that everyone else has access to.

When to use Custom ML:

• For Unique Data: Specialized sensor data, proprietary manufacturing logs.
• For Complex Requirements: When you need to optimize for a very specific type of error (e.g., "False Negatives" in a cancer screen are much worse than "False Positives").

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3. The Comparison Table

Feature	Managed AI (e.g., Comprehend)	Custom ML (SageMaker)
Complexity	Low	High
Effort	Low (API Call)	High (Data prep/Training)
Control	None (AWS decides)	Total
Cost (Setup)	Near Zero	High (Engineering hours)
Best For	General Business Tasks	Unique/Proprietary Tasks

graph TD
    A[Business Problem] --> B{Is the task 'Standard'?}
    B -->|Yes| C[Managed AI Service: FAST]
    B -->|No| D{Do you have ML Experts?}
    D -->|Yes| E[SageMaker: CUSTOM]
    D -->|No| F[Managed AI + Feedback Loop]
    E --> G[High Competitive Advantage]
    C --> H[Low Operational Cost]

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4. Summary: AWS's Recommendation

AWS follows a "Managed First" philosophy.

1. Look if a Managed Service (Rekognition, Bedrock, etc.) can solve 90% of your problem.
2. If yes, use it and spend your time on your product, not on the math.
3. Only move to SageMaker when the managed service is no longer accurate enough or when your business survival depends on owning the specific model.

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Exercise: Choose the Path

A boutique winery wants to build an app that lets users take a photo of a wine bottle and instantly see the price and tasting notes. They have a database of 50,000 wine labels. They have one developer who knows JavaScript. Which path should they take?

• A. Custom ML (Build a model in SageMaker to recognize labels).
• B. Managed AI (Use Amazon Rekognition's "Custom Labels" feature).
• C. Generative AI (Use Amazon Bedrock to 'dream' what the wine looks like).
• D. Analytics (Use QuickSight to visualize the wine data).

The Answer is B! "Custom Labels" in Rekognition is a "Middle-Ground." It is a managed service that allows you to provide a small amount of your own data to an existing AWS model. This is the fastest, cheapest path for the winery!

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Knowledge Check

What's Next?

Now that we know the "Philosophy," let's look at the "Process." How do we pick the exact service? Find out in Lesson 3: Choosing the right service for the problem.