AWS Developer Center

Doing ML locally is one thing—with a fairly low barrier to entry.

Doing it in production? That’s where things get tricky.

You must consider scalability, compute costs, security, monitoring, and automation. Things that don’t matter much when running experiments on your laptop suddenly become huge bottlenecks when moving to the cloud.

And on top of all this, a learning curve is associated with becoming proficient at cloud services.

AWS Developer Center is solving all these problems for you.

Let’s dive in to learn more!

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What is AWS Developer Center?

In a gist, it’s a platform for any developer to get hands-on learning resources—tutorials, live streams, podcasts, sample code and more, that walk you through real problems developers face when working with AWS.

• There are resources for people building GenAI solutions.

• There are resources for people building ML models or doing MLOps.

• There are resources for people working with data pipelines.

• And many many more.

Let’s talk about GenAI and ML since they align with what we cover here.

1) Generative AI on AWS

There are multiple ways to learn how to build GenAI apps on AWS:

• Amazon Bedrock provides several foundational models to work with and deploy—from Amazon’s Titan to leading Al startups - Al21Labs, Anthropic, cohere, Meta, StabilityAl.

• You can also use Amazon SageMaker JumpStart. It has hundreds of built-in algorithms and pre-trained models. The selected model can then be fine-tuned with additional training in AWS to suit the application’s needs.

You can start with these sample apps:

2) Data and ML on AWS

Similar resources exist if you want to do Data and ML on AWS:

There are resources covering beginner tutorials for the fundamentals and advanced guides to take your skills to the next level.

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Amazon Q

If you spend time writing Lambda functions, managing EC2 instances, or configuring infrastructure on AWS, Amazon Q is worth checking out.

Unlike generic AI coding assistants, Amazon Q actually understands AWS services. It can:

• Generate and debug AWS-specific code—Need an IAM policy? Just describe it, and Q generates it for you.

• Explain AWS errors—Q gives you an instant explanation and fix.

• Work inside VS Code—You don’t need to switch tabs, just ask questions and get suggestions directly in your IDE.

If you write AWS-heavy code, this tool can cut your debugging time in half.

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Conclusion

AWS Developer Center isn’t just for AI.

It also covers core data and infrastructure topics that are critical when working with machine learning at scale:

• Optimizing costs.

• Building scalable data pipelines.

• Automating deployments with CI/CD.

Of course, moving from a local ML setup to a scalable cloud pipeline isn’t easy.

AWS Developer Center gives you the blueprints and hands-on tutorials to make that jump faster—without spending weeks reading docs.

If you want to check it out, here’s the link: AWS Developer Center​.

Thanks to AWS for partnering with us on today’s issue and creating all these resources for free.

Thanks for reading!