6 AWS machine learning services you should be using

Your app just made a sale ... before the customer even realized they needed the product.

How? It analyzed browsing habits, predicted demand, and personalized the experience—all in real time.

That's not luck. That's machine learning (ML) in action.

Now, imagine that same intelligence detecting fraud, powering chatbots, or automating workflows. The possibilities are huge.

And that’s why ML isn’t just another tool—it’s the foundation of today’s smartest technology.

With businesses prioritizing real-time personalization, automation, and AI-driven decisions, ML is no longer optional for developers. If you want to build scalable, adaptive applications, now’s the time to dive in.

The good news is that getting into ML doesn’t have to be complicated. AWS has tools that let you fine-tune models or drop in prebuilt AI features—so you can focus on building smarter, faster, and more scalable apps without getting stuck in the weeds.

Here’s what we’ll cover in today’s newsletter:

• AWS ML stack explained: Tools for every stage, from plug-and-play to advanced frameworks.

• 6 AWS ML services you need to know: Like Rekognition, SageMaker, and Bedrock.

• Real-world use cases: See how ML can transform your apps and workflows.

Onward!